fix(i18n): correct JSON syntax in German and French translations
Fixed JSON syntax errors in 8 translation files (German and French for researcher, implementer, leader, about pages). Removed extra closing braces that were breaking translation loading on production. All translations now validated with json.tool and working correctly on all audience pages. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
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docs/governance/ALEXANDER-PATTERN-RULES.md
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docs/governance/ALEXANDER-PATTERN-RULES.md
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# Christopher Alexander Pattern Language Rules for Tractatus
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**Created**: 30 October 2025
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**Purpose**: Apply Christopher Alexander's architectural principles to AI governance framework design
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**Source**: The Timeless Way of Building, A Pattern Language, The Nature of Order (15 Properties of Life)
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**Status**: Draft for review and integration into instruction-history.json
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---
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## Background: Alexander's Principles Applied to AI Governance
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Christopher Alexander's work on pattern languages, living processes, and the "quality without a name" provides profound insights for designing AI governance systems that feel coherent, alive, and resilient rather than brittle and bureaucratic.
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### Key Concepts from Alexander's Work:
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**1. The 15 Fundamental Properties of Life** (from "The Nature of Order"):
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- Strong Centers
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- Boundaries
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- Levels of Scale
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- Alternating Repetition
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- Positive Space
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- Good Shape
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- Local Symmetries
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- Deep Interlock and Ambiguity
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- Contrast
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- Gradients
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- Roughness
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- Echoes
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- The Void
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- Simplicity and Inner Calm
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- Not-Separateness
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**2. Structure-Preserving Transformations**:
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- Changes that enhance wholeness while maintaining system coherence
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- Contrasted with structure-destroying transformations that fracture integrity
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**3. Living Process**:
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- Systems evolve through use, feedback, and adaptation
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- Growth emerges from context, not imposed design
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- Patterns reinforce each other organically
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**4. Pattern Language Methodology**:
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- Interconnected patterns that solve recurring problems
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- Each pattern supported by and supporting others
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- Creates coherent whole greater than sum of parts
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**5. Quality Without a Name**:
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- Wholeness, coherence, aliveness
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- Systems feel "right" - neither mechanical nor chaotic
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- Emerges from deep structural properties, not surface aesthetics
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### Why This Matters for Tractatus:
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Governance frameworks often feel like compliance theatre because they're designed as **fixed structures** rather than **living processes**. Alexander's principles help us build governance that:
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- **Feels coherent** - Services reinforce each other rather than conflicting
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- **Adapts organically** - Framework evolves from real failures (27027 Incident → CrossReferenceValidator enhancement)
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- **Maintains wholeness** - Changes preserve interpretability of prior audit logs
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- **Operates on gradients** - Context pressure, persistence levels, not binary yes/no
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- **Integrates deeply** - Governance woven into deployment architecture, not bolted on
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---
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## The Five Alexander-Inspired Rules
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### inst_090: Centers Reinforce Centers (Deep Interlock)
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**Classification**:
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```json
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{
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"id": "inst_090",
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"text": "Six governance services must reinforce each other through mutual validation, creating deep interlock rather than isolated enforcement",
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"quadrant": "STRATEGIC",
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"persistence": "HIGH",
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"temporal_scope": "PERMANENT",
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"verification_required": "REQUIRED",
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"explicitness": 0.90,
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"source": "architectural_principle",
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"parameters": {
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"services": ["BoundaryEnforcer", "CrossReferenceValidator", "MetacognitiveVerifier", "ContextPressureMonitor", "InstructionPersistenceClassifier", "PluralisticDeliberationOrchestrator"],
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"principle": "deep_interlock"
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},
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"active": true
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}
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```
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**Principle**: In Alexander's work, "strong centers" are elements that draw energy and coherence from surrounding elements. Centers that reinforce each other create deep interlock - mutual support where the whole becomes greater than the sum.
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**Operational Description**:
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When one governance service activates, it should strengthen and be strengthened by others:
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- **BoundaryEnforcer** flags values conflict → **PluralisticDeliberationOrchestrator** mediates → **CrossReferenceValidator** checks precedents → **MetacognitiveVerifier** validates reasoning chain → **Audit log** captures full governance event
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- **ContextPressureMonitor** detects ELEVATED pressure → **CrossReferenceValidator** intensifies instruction checks → **MetacognitiveVerifier** verifies complex operations → Services adapt behavior based on pressure gradient
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**Example** (27027 Incident):
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- User instruction: "Use MongoDB port [custom-port]" (explicit)
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- **InstructionPersistenceClassifier**: Stores as SYSTEM/HIGH
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- **ContextPressureMonitor**: Detects 53.5% context pressure
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- **CrossReferenceValidator**: Catches AI attempting default default port
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- **BoundaryEnforcer**: Blocks action (violates explicit instruction)
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- **Audit trail**: Documents full chain for regulatory compliance
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Each service reinforced the others. The governance event emerged from their interlock, not from any single service.
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**Rationale**:
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- **Resilience**: If one service misses an issue, others catch it
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- **Coherence**: Governance feels integrated, not fragmented
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- **Auditability**: Regulators see coordinated enforcement, not isolated checks
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- **Evolution**: Services learn from each other's patterns
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**Anti-Pattern** (What to Avoid):
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- Services operating in silos with no coordination
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- Governance checks happening independently with no mutual validation
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- Audit logs showing isolated service activations rather than coordinated responses
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**Related Instructions**:
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- inst_064: Framework fade detection (ensures interlock maintains)
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- inst_078: Framework audit for conversational responses (orchestrates all 6 services)
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- inst_082: Framework statistics visibility (monitors service coordination)
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**Verification**:
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When analyzing audit logs, ask: "Did multiple services coordinate on this governance event, or did one service act alone?" Lone service activations suggest weak interlock.
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---
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### inst_091: Structure-Preserving Transformations Only
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**Classification**:
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```json
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{
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"id": "inst_091",
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"text": "Framework changes must preserve wholeness - existing audit logs remain interpretable, prior governance decisions remain valid, instruction precedents maintain authority",
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"quadrant": "STRATEGIC",
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"persistence": "HIGH",
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"temporal_scope": "PERMANENT",
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"verification_required": "MANDATORY",
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"explicitness": 0.95,
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"source": "architectural_principle",
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"parameters": {
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"principle": "structure_preserving_transformation",
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"preservation_targets": ["audit_logs", "governance_decisions", "instruction_precedents"]
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},
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"active": true
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}
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```
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**Principle**: Alexander distinguishes between structure-preserving transformations (enhance wholeness) and structure-destroying transformations (fracture integrity). Good design evolves through transformations that honor what exists while improving it.
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**Operational Description**:
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Before making framework changes, verify they preserve:
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1. **Audit Log Interpretability**: Can we still understand governance decisions from 6 months ago?
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2. **Instruction Authority**: Do prior HIGH persistence instructions remain enforceable?
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3. **Service Coordination**: Do existing service integrations continue working?
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4. **Pattern Recognition**: Can we identify governance patterns across old and new audit logs?
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**Example** (Structure-Preserving):
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Adding `MetacognitiveVerifier` selective mode (inst_084) was structure-preserving because:
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- ✅ Prior audit logs remain valid
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- ✅ Service still triggers on same conditions
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- ✅ Existing instructions about verification still apply
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- ✅ Just reduces unnecessary activations (performance improvement)
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**Example** (Structure-Destroying - Avoided):
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If we changed `BoundaryEnforcer` to use completely different boundary definitions:
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- ❌ Prior audit logs become uninterpretable (what did "boundary violation" mean then?)
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- ❌ Existing instructions about boundaries lose meaning
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- ❌ Regulatory evidence chain breaks (can't prove continuous enforcement)
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- ❌ Framework coherence fractures
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**Rationale**:
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- **Regulatory Compliance**: Auditors need continuous evidence trail
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- **Organizational Learning**: Can't learn from patterns if definitions keep changing
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- **Framework Coherence**: Changes should enhance, not replace
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- **Institutional Memory**: Governance decisions compound over time
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**Test Questions Before Framework Changes**:
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1. Can I still interpret last month's audit logs using today's framework?
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2. Do existing HIGH persistence instructions remain enforceable?
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3. Would a regulator see continuous governance or discontinuous patches?
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4. If I rolled back this change, would the system still be coherent?
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If any answer is "no," the transformation is structure-destroying and should be redesigned.
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**Related Instructions**:
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- inst_063_CONSOLIDATED: Pre-action check architecture (enforces verification before changes)
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- inst_076: Instruction history immutability (preserves governance decisions)
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- inst_080: Dependency license compliance (structural preservation at dependency level)
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**Verification**: MANDATORY - All framework architectural changes must demonstrate structure-preservation via audit log continuity analysis.
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---
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### inst_092: Gradients Over Binary Switches
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**Classification**:
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```json
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{
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"id": "inst_092",
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"text": "Governance operates on gradients (NORMAL/ELEVATED/HIGH/CRITICAL context pressure, LOW/MEDIUM/HIGH persistence) rather than binary yes/no switches",
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"quadrant": "STRATEGIC",
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"persistence": "HIGH",
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"temporal_scope": "PERMANENT",
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"verification_required": "REQUIRED",
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"explicitness": 0.88,
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"source": "architectural_principle",
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"parameters": {
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"principle": "gradients_not_binary",
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"examples": ["context_pressure_levels", "persistence_levels", "verification_requirements"]
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},
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"active": true
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}
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```
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**Principle**: Alexander emphasizes gradients as fundamental to living structure. Natural systems don't use binary switches - they transition gradually (dawn to daylight, seasons, growth). Gradients create coherence and allow nuanced responses.
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**Operational Description**:
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Tractatus implements gradients at multiple levels:
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**1. Context Pressure Gradient**:
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- NORMAL (0-25%): Standard operation
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- ELEVATED (25-50%): Increase instruction validation
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- HIGH (50-75%): Critical instruction enforcement
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- CRITICAL (75-100%): Session handoff required
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Not binary "too much context" or "fine" - nuanced levels with different service behaviors.
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**2. Persistence Gradient**:
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- LOW: Session-specific, fades after completion
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- MEDIUM: Project-specific, active until explicitly deprecated
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- HIGH: Permanent, requires structured deactivation process
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Not binary "remember" or "forget" - gradient of institutional memory.
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**3. Verification Requirement Gradient**:
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- OPTIONAL: Service may verify if contextually relevant
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- REQUIRED: Service must verify before proceeding
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- MANDATORY: Multiple services must verify + human approval required
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Not binary "check" or "don't check" - gradient of governance intensity.
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**Example** (Context Pressure Gradient in Action):
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At 20% pressure (NORMAL):
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- CrossReferenceValidator runs standard checks
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- MetacognitiveVerifier operates in selective mode
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- BoundaryEnforcer monitors passively
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At 55% pressure (HIGH):
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- CrossReferenceValidator intensifies - every stored instruction validated
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- MetacognitiveVerifier activates on all multi-step operations
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- BoundaryEnforcer flags marginal cases for review
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Same framework, different intensity based on gradient position.
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**Rationale**:
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- **Nuanced Response**: Match governance intensity to risk level
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- **Avoid Alert Fatigue**: Binary switches create crying-wolf problem
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- **Natural Feel**: Gradients feel organic, not mechanical
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- **Adaptive Behavior**: Services tune themselves to context
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**Anti-Pattern** (Binary Switches):
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- "Context is fine" vs. "context is bad" (no nuance)
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- "Instruction is important" vs. "instruction doesn't matter" (no gradient of persistence)
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- "Must verify everything always" vs. "verify nothing" (exhausting or negligent)
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**Related Instructions**:
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- inst_006: ContextPressureMonitor gradient thresholds
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- inst_043: Instruction persistence classification (LOW/MEDIUM/HIGH)
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- inst_084: MetacognitiveVerifier selective mode (verification intensity gradient)
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**Verification**: When designing new framework features, ask: "Can this operate on a gradient rather than a binary switch?"
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---
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### inst_093: Living Process Over Fixed Design
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**Classification**:
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```json
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{
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"id": "inst_093",
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"text": "Framework evolves through real-world use and feedback, not top-down specification - governance grows from failures and successes, not predetermined plans",
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"quadrant": "STRATEGIC",
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"persistence": "HIGH",
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"temporal_scope": "PERMANENT",
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"verification_required": "REQUIRED",
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"explicitness": 0.85,
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"source": "architectural_principle",
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"parameters": {
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"principle": "living_process",
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"evolution_triggers": ["real_failures", "audit_log_analysis", "governance_gaps", "user_feedback"]
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},
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"active": true
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}
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```
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**Principle**: Alexander's "living process" means systems grow organically through use, not through master plans imposed from above. Each change responds to actual context, preserving what works while addressing what doesn't.
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**Operational Description**:
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Tractatus framework evolution follows living process:
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**1. Real Failure Occurs**: 27027 Incident - AI used default port (27017) despite explicit instruction (27027)
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**2. Framework Responds**: CrossReferenceValidator enhanced to catch instruction/action conflicts
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**3. Governance Grows**: New capability emerges from actual failure, not theoretical risk
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**4. Pattern Reinforces**: Service integration deepens (CrossReferenceValidator ↔ InstructionPersistenceClassifier ↔ ContextPressureMonitor)
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**Example** (Living Process):
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**Cultural DNA Rules** (inst_085-089):
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- Emerged from analyzing actual writing patterns across sessions
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- Identified "comprehensive AI governance" problem from real documentation review
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- Created rules responding to observed needs, not theoretical frameworks
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- Rules validated through application, then codified
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**Framework Fade Detection** (inst_064):
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- Discovered services going unused despite being "initialized"
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- Created architectural solution (fade detection + recovery)
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- Rule emerged from observed problem, not predetermined design
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**Contrast with Fixed Design** (Anti-Pattern):
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Fixed design approach would be:
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1. Define "complete governance framework" upfront
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2. Specify all services in detail before deployment
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3. Resist changes (might break master plan)
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4. Framework stays static regardless of real-world performance
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Living process approach (Tractatus):
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1. Deploy minimal viable governance (6 core services)
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2. Monitor actual operation via audit logs
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3. Enhance when real failures occur
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4. Framework grows smarter through experience
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**Rationale**:
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- **Context-Responsive**: Solutions fit actual problems, not theoretical ones
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- **Sustainable**: Changes emerge from necessity, not arbitrary decisions
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- **Institutional Learning**: Organization gets smarter through governance evolution
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- **Avoid Over-Engineering**: Only build what reality demands
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**Operational Guidance**:
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When considering framework changes, ask:
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- What real failure/gap prompted this? (Not: "What might go wrong?")
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- What do audit logs show about current behavior?
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- Does this preserve existing patterns that work?
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- Is this the minimal effective response?
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**Related Instructions**:
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- inst_064: Framework fade (living process caught degradation pattern)
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- inst_076: Instruction history immutability (preserves learning)
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- inst_091: Structure-preserving transformations (evolution preserves wholeness)
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**Verification**: Framework changes should reference specific audit log patterns, real incidents, or observed governance gaps - not theoretical risks.
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---
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### inst_094: Not-Separateness (Framework Integration)
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**Classification**:
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```json
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{
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"id": "inst_094",
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"text": "Governance must be woven into AI deployment architecture, not bolted on as separate compliance layer - if AI can execute without governance validation, framework is separate (and will be bypassed)",
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"quadrant": "STRATEGIC",
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"persistence": "HIGH",
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"temporal_scope": "PERMANENT",
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"verification_required": "MANDATORY",
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"explicitness": 0.93,
|
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"source": "architectural_principle",
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"parameters": {
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"principle": "not_separateness",
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"integration_test": "can_ai_bypass_governance"
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},
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"active": true
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}
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```
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**Principle**: Alexander's "not-separateness" is about deep integration - elements connected so thoroughly that separation is impossible without destroying the whole. In governance terms: enforcement integrated into execution architecture, not layered on top.
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**Operational Description**:
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Tractatus achieves not-separateness through **architectural enforcement**:
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**Integrated** (Not-Separateness):
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```
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User Request → PreToolUse Hook → BoundaryEnforcer check →
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CrossReferenceValidator → MetacognitiveVerifier →
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Action executes ONLY if governance passes → Audit log
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```
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**Separated** (Bolt-On - Anti-Pattern):
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```
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User Request → Action executes →
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Later: Review logs, hope AI followed policies,
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governance is separate compliance check
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```
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**The Integration Test**:
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Ask: "Can AI execute this action without governance services validating it?"
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- **If YES**: Framework is separate (can be bypassed, ignored, or "optimized away")
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- **If NO**: Framework is integrated (not-separateness achieved)
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**Example** (Not-Separateness):
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**BoundaryEnforcer + PreToolUse Hook**:
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- Hook architecture intercepts file writes BEFORE execution
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- BoundaryEnforcer validates against stored instructions
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- If validation fails, write NEVER HAPPENS
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- Governance and execution are inseparable
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**Contrast** (Separateness):
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- AI writes files freely
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- Separate compliance service reviews logs afterward
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- Governance is monitoring, not enforcement
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- Can be disabled, bypassed, or ignored without breaking deployment
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**Example** (27027 Incident):
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**Separated approach** would be:
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- AI connects to default port
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- Later log review catches mistake
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- Damage already done (wrong database accessed)
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**Integrated approach** (Tractatus):
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- CrossReferenceValidator checks port against stored instruction BEFORE connection
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- Detects conflict (27027 stored, 27017 attempted)
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- Blocks connection architecturally
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- Governance inseparable from execution
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**Rationale**:
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- **Enforcement Not Theatre**: Separated governance is performative, integrated governance is structural
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- **Regulatory Credibility**: Auditors recognize architectural enforcement vs. policy compliance
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- **Failure Prevention**: Integrated governance blocks failures before they occur
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- **Cannot Be Bypassed**: Not-separateness means governance can't be "turned off" without breaking deployment
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**Architectural Implications**:
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1. **Hooks over Monitoring**: PreToolUse hooks intercept actions, not review them afterward
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2. **Blocking over Logging**: Services can prevent execution, not just document it
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3. **Validation in Critical Path**: Governance checks must complete before action proceeds
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||||
4. **Shared Data Structures**: Services and AI share instruction database, audit logs (not separate systems)
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**Test Questions**:
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||||
|
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Before deploying new capability:
|
||||
1. Can AI bypass governance to execute this action?
|
||||
2. Is governance in the critical path or a side channel?
|
||||
3. If governance service fails, does deployment fail (integrated) or continue (separated)?
|
||||
4. Can we disable governance without code changes (separated) or is it architecturally required (integrated)?
|
||||
|
||||
If governance is separable, redesign for not-separateness.
|
||||
|
||||
**Related Instructions**:
|
||||
- inst_063_CONSOLIDATED: Pre-action checks (architectural integration)
|
||||
- inst_072: Defense-in-depth (multiple integration layers)
|
||||
- inst_078: Framework audit responses (governance integrated into conversation flow)
|
||||
|
||||
**Verification**: MANDATORY - New deployment capabilities must demonstrate governance integration via architecture review showing services in critical execution path.
|
||||
|
||||
---
|
||||
|
||||
## Integration Guidance
|
||||
|
||||
These five rules supplement the existing Cultural DNA rules (inst_085-089) by providing **architectural principles** for framework evolution:
|
||||
|
||||
**Cultural DNA Rules** (inst_085-089): Guide content creation, communication, documentation
|
||||
**Alexander Pattern Rules** (inst_090-094): Guide framework architecture, service design, system evolution
|
||||
|
||||
Together they create coherent governance that feels alive, integrated, and resilient.
|
||||
|
||||
### Next Steps:
|
||||
|
||||
1. **Review** these rules for accuracy and alignment with Tractatus principles
|
||||
2. **Validate** examples and operational descriptions
|
||||
3. **Integrate** into .claude/instruction-history.json (inst_090-094)
|
||||
4. **Activate** in framework operation
|
||||
5. **Monitor** via audit logs for effectiveness
|
||||
|
||||
---
|
||||
|
||||
## References
|
||||
|
||||
- Christopher Alexander, "The Timeless Way of Building" (1979)
|
||||
- Christopher Alexander, "A Pattern Language" (1977)
|
||||
- Christopher Alexander, "The Nature of Order" (2002-2004) - 15 Properties of Life
|
||||
- Gabriel, Richard P., "Patterns of Software: Tales from the Software Community" (1996)
|
||||
- Tractatus instruction-history.json (inst_001-089)
|
||||
- Tractatus Cultural DNA rules (inst_085-089)
|
||||
|
||||
---
|
||||
|
||||
**Document Status**: Draft awaiting review
|
||||
**Created**: 30 October 2025
|
||||
**Author**: Claude (Tractatus AI Agent)
|
||||
**Approval Required**: Yes - before integration into instruction-history.json
|
||||
98
docs/outreach/DALLE-PROMPTS-FACEBOOK.md
Normal file
98
docs/outreach/DALLE-PROMPTS-FACEBOOK.md
Normal file
|
|
@ -0,0 +1,98 @@
|
|||
# DALL-E Prompts for Tractatus Facebook Images
|
||||
|
||||
**Purpose**: Copy-paste these prompts directly into DALL-E or Midjourney
|
||||
**Date**: 30 October 2025
|
||||
|
||||
---
|
||||
|
||||
## Prompt 1: Profile Picture (400×400px)
|
||||
|
||||
```
|
||||
Create a minimal professional logo icon for a technical AI governance framework, 400x400 pixels square.
|
||||
|
||||
Design: Large letter "T" in the center, modern sans-serif font, cyan color (#64ffda). Background is radial gradient from cyan (#64ffda) to deep blue (#0ea5e9). Around the T, six small dots arranged in a hexagon, each colored differently: green (#10b981), indigo (#6366f1), purple (#8b5cf6), amber (#f59e0b), pink (#ec4899), teal (#14b8a6). The dots represent six governance services.
|
||||
|
||||
Style: Clean, technical, B2B SaaS aesthetic. Similar to Linear app icon or Stripe branding. Must be recognizable when scaled to 40x40px. Professional, not playful. Tech governance feel, not consumer startup.
|
||||
|
||||
High contrast, bold design, must work at tiny sizes. Minimal and sophisticated.
|
||||
```
|
||||
|
||||
**After generation**: Save as `Tractatus-Facebook-Profile.png`
|
||||
|
||||
---
|
||||
|
||||
## Prompt 2: Cover Photo (820×312px)
|
||||
|
||||
```
|
||||
Create a professional Facebook cover photo for a technical AI governance framework, 820x312 pixels horizontal layout.
|
||||
|
||||
Background: Dark slate color (#0f172a), subtle gradient to darker at edges.
|
||||
|
||||
Top left area (readable on all devices):
|
||||
- Title text: "TRACTATUS FRAMEWORK" in large bold font, cyan color (#64ffda)
|
||||
- Below it: "Architectural Constraints for Plural Moral Values" in light gray (#94a3b8)
|
||||
|
||||
Center/bottom area: Simple architectural network diagram showing six governance services
|
||||
- Central cyan node (#64ffda) with six colored circles connected to it
|
||||
- Six circles arranged in hexagonal pattern around the center
|
||||
- Circle colors: green (#10b981), indigo (#6366f1), purple (#8b5cf6), amber (#f59e0b), pink (#ec4899), teal (#14b8a6)
|
||||
- Connection lines between nodes: thin, semi-transparent cyan
|
||||
- Clean network diagram aesthetic, like technical architecture visualization
|
||||
|
||||
Bottom left: "agenticgovernance.digital" in small gray text (#64748b)
|
||||
|
||||
Style: Technical B2B aesthetic. Similar to Stripe's technical illustrations or Vercel's product imagery. Dark background, cyan accents, modern and minimal. Professional governance/security feel. NOT playful, NOT consumer-focused. Typography should be modern sans-serif (Inter or SF Pro style). High contrast for readability.
|
||||
```
|
||||
|
||||
**After generation**: Save as `Tractatus-Facebook-Cover.png`
|
||||
|
||||
---
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
### If DALL-E produces wrong dimensions:
|
||||
- Request "exactly 400x400 pixels" or "exactly 820x312 pixels"
|
||||
- Or generate larger and crop in post-processing
|
||||
|
||||
### If colors are wrong:
|
||||
- Regenerate with emphasis on "use exact hex colors provided"
|
||||
- May need to adjust in Figma/Photoshop afterward
|
||||
|
||||
### If text is unreadable:
|
||||
- Request "high contrast text, must be readable at small sizes"
|
||||
- Emphasize "professional typography, bold and clear"
|
||||
|
||||
### If too cluttered:
|
||||
- Regenerate with "more minimal, more whitespace"
|
||||
- Request "clean and simple, like Stripe design"
|
||||
|
||||
---
|
||||
|
||||
## Alternative: Midjourney Format
|
||||
|
||||
### Profile Picture (Midjourney):
|
||||
```
|
||||
minimal professional logo icon, large letter T in center, cyan color, radial gradient background cyan to blue, six small dots in hexagon around T colored green indigo purple amber pink teal, clean technical B2B aesthetic, similar to Linear app icon, 400x400 pixels --ar 1:1 --style raw --v 6
|
||||
```
|
||||
|
||||
### Cover Photo (Midjourney):
|
||||
```
|
||||
professional facebook cover photo 820x312px, dark slate background, title "TRACTATUS FRAMEWORK" in large cyan text, subtitle "Architectural Constraints for Plural Moral Values" light gray, center shows network diagram with cyan central node connected to six colored circles in hexagon green indigo purple amber pink teal, bottom text "agenticgovernance.digital", technical B2B style like Stripe or Vercel, minimal professional --ar 82:31 --style raw --v 6
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Quick Copy-Paste Instructions
|
||||
|
||||
1. Go to https://chat.openai.com (ChatGPT Plus with DALL-E)
|
||||
2. Copy Prompt 1, paste, generate
|
||||
3. Download as `Tractatus-Facebook-Profile.png`
|
||||
4. Copy Prompt 2, paste, generate
|
||||
5. Download as `Tractatus-Facebook-Cover.png`
|
||||
6. Verify dimensions and quality
|
||||
7. Upload to Facebook
|
||||
|
||||
---
|
||||
|
||||
**Location**: Save both images to `/docs/outreach/`
|
||||
**Full Design Brief**: See `FACEBOOK-IMAGE-DESIGN-BRIEF.md` for complete specifications
|
||||
125
docs/outreach/FACEBOOK-IMAGE-DESIGN-BRIEF.md
Normal file
125
docs/outreach/FACEBOOK-IMAGE-DESIGN-BRIEF.md
Normal file
|
|
@ -0,0 +1,125 @@
|
|||
# Facebook Image Design Brief - Tractatus Framework
|
||||
|
||||
**Purpose**: Professional social media branding for Phase 0 launch
|
||||
**Requires**: Proper image generation tool (DALL-E/Midjourney) or professional designer
|
||||
**Date**: 30 October 2025
|
||||
|
||||
---
|
||||
|
||||
## What Went Wrong
|
||||
|
||||
Previous attempt used ImageMagick which produces crude, unprofessional graphics:
|
||||
- Pixelated text and circles
|
||||
- Poor gradient handling
|
||||
- "Programmer art" aesthetic
|
||||
- Not suitable for professional B2B branding
|
||||
|
||||
---
|
||||
|
||||
## Image 1: Profile Picture (400×400px)
|
||||
|
||||
**Concept**: Clean, bold "T" icon that works at tiny sizes
|
||||
|
||||
**Design**:
|
||||
- Central "T" letter, modern sans-serif, cyan (#64ffda)
|
||||
- Radial gradient background: cyan to blue (#64ffda → #0ea5e9)
|
||||
- Six tiny dots in hexagon around T (green, indigo, purple, amber, pink, teal)
|
||||
- Must be recognizable at 40×40px (mobile size)
|
||||
|
||||
**Style Reference**: Linear app icon, Stripe logo - minimal, technical, B2B
|
||||
|
||||
**DALL-E Prompt**:
|
||||
```
|
||||
Create a minimal professional logo icon, 400x400px square. Large letter "T" in center, modern sans-serif font, cyan color (#64ffda). Radial gradient background from cyan to deep blue. Six small dots arranged in hexagon around the T, colors: green, indigo, purple, amber, pink, teal. Clean technical B2B aesthetic, similar to Linear app icon. Must work at small sizes. Professional not playful.
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Image 2: Cover Photo (820×312px)
|
||||
|
||||
**Concept**: Simple architecture diagram showing six governance services
|
||||
|
||||
**Layout**:
|
||||
```
|
||||
┌────────────────────────────────────────┐
|
||||
│ │
|
||||
│ TRACTATUS FRAMEWORK │
|
||||
│ Architectural Constraints for │
|
||||
│ Plural Moral Values │
|
||||
│ │
|
||||
│ [6 connected nodes diagram] │
|
||||
│ │
|
||||
│ agenticgovernance.digital │
|
||||
└────────────────────────────────────────┘
|
||||
```
|
||||
|
||||
**Design Elements**:
|
||||
1. Background: Dark slate (#0f172a)
|
||||
2. Title: "TRACTATUS FRAMEWORK" - large, bold, cyan (#64ffda)
|
||||
3. Subtitle: "Architectural Constraints for Plural Moral Values" - light gray (#94a3b8)
|
||||
4. Center: Six colored circles connected to central cyan node
|
||||
- Hexagonal arrangement
|
||||
- Colors: green, indigo, purple, amber, pink, teal
|
||||
- Thin cyan connection lines
|
||||
- Clean network diagram style
|
||||
5. Footer: "agenticgovernance.digital" - small gray text
|
||||
|
||||
**Style Reference**: Stripe technical illustrations, Vercel product imagery - dark, minimal, technical
|
||||
|
||||
**DALL-E Prompt**:
|
||||
```
|
||||
Create professional Facebook cover photo 820x312px. Dark slate background (#0f172a). Top left: "TRACTATUS FRAMEWORK" in large bold cyan text (#64ffda), below it "Architectural Constraints for Plural Moral Values" in light gray. Center: simple network diagram with central cyan node connected to six colored circles (green, indigo, purple, amber, pink, teal) arranged in hexagon. Thin cyan connection lines. Bottom: "agenticgovernance.digital" in small gray. Technical B2B aesthetic like Stripe or Vercel. Clean, minimal, professional.
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Color Palette (Must Use Exact Colors)
|
||||
|
||||
```
|
||||
Primary: #64ffda (cyan)
|
||||
Secondary: #0ea5e9 (blue)
|
||||
Background: #0f172a (dark slate)
|
||||
Text: #f8fafc (white)
|
||||
#94a3b8 (light gray)
|
||||
#64748b (medium gray)
|
||||
|
||||
Six Service Colors:
|
||||
#10b981 (green) - Boundary
|
||||
#6366f1 (indigo) - Instruction
|
||||
#8b5cf6 (purple) - Validator
|
||||
#f59e0b (amber) - Pressure
|
||||
#ec4899 (pink) - Metacognitive
|
||||
#14b8a6 (teal) - Deliberation
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Design Principles
|
||||
|
||||
**DO**:
|
||||
✅ Clean, minimal, professional
|
||||
✅ Technical B2B aesthetic (not consumer/startup)
|
||||
✅ High contrast (readable on all devices)
|
||||
✅ Use exact Tractatus colors
|
||||
✅ Works at small sizes
|
||||
|
||||
**DON'T**:
|
||||
❌ Cluttered or busy
|
||||
❌ Playful consumer aesthetic
|
||||
❌ Complex illustrations
|
||||
❌ Too much text
|
||||
❌ Generic stock imagery
|
||||
|
||||
---
|
||||
|
||||
## Next Steps
|
||||
|
||||
**Recommended**: Use DALL-E or Midjourney with prompts above
|
||||
|
||||
**Alternative**: Send this brief to professional designer
|
||||
|
||||
**Files Needed**:
|
||||
- Tractatus-Facebook-Profile.png (400×400px)
|
||||
- Tractatus-Facebook-Cover.png (820×312px)
|
||||
|
||||
Both must be professional quality suitable for B2B tech branding.
|
||||
68
docs/outreach/FACEBOOK-POST-CORE-VALUES.md
Normal file
68
docs/outreach/FACEBOOK-POST-CORE-VALUES.md
Normal file
|
|
@ -0,0 +1,68 @@
|
|||
# Facebook Post: Tractatus Core Values Summary
|
||||
|
||||
**Date**: 30 October 2025
|
||||
|
||||
---
|
||||
|
||||
## The 4 Core Values
|
||||
|
||||
### Version 1: Direct & Principled (~200 words)
|
||||
|
||||
**Tractatus Framework: Four Core Values**
|
||||
|
||||
When we built Tractatus, we started with principles, not features.
|
||||
|
||||
🛡️ **Sovereignty**: Humans control decisions affecting their data and values. No AI paternalism—you decide, AI executes.
|
||||
|
||||
🔍 **Transparency**: Every governance decision is explainable and auditable. No black boxes. When AI is blocked from an action, you know why.
|
||||
|
||||
⚠️ **Harmlessness**: AI systems fail safely. When uncertain, they ask—they don't assume. Prevents drift through architectural constraints, not hoped-for behavior.
|
||||
|
||||
🤝 **Community**: Open-source, vendor-free, accessible. AI governance is a collective challenge, not a competitive advantage to hoard.
|
||||
|
||||
**What we reject**: Dark patterns, hidden optimization goals, irreversible actions without consent, paywalls on safety.
|
||||
|
||||
**Our principle**: When in doubt, we choose human agency over AI capability. Every time.
|
||||
|
||||
Rooted in Te Tiriti o Waitangi principles—governance that honors plural values, not imposed hierarchies.
|
||||
|
||||
Learn more: https://agenticgovernance.digital/about/values.html
|
||||
|
||||
---
|
||||
|
||||
## Version 2: Short & Punchy (Twitter/LinkedIn)
|
||||
|
||||
**Tractatus Values in 4 Lines**
|
||||
|
||||
🛡️ Sovereignty: Humans decide, AI executes
|
||||
🔍 Transparency: No black boxes, full audit trails
|
||||
⚠️ Harmlessness: AI asks when uncertain, doesn't guess
|
||||
🤝 Community: Open-source, vendor-free
|
||||
|
||||
Human agency > AI capability. Always.
|
||||
|
||||
https://agenticgovernance.digital/about/values.html
|
||||
|
||||
---
|
||||
|
||||
## Version 3: NZ/Pacific Audience
|
||||
|
||||
**AI Governance Rooted in Te Tiriti Principles**
|
||||
|
||||
Tractatus is built on four values—principles learned from Te Tiriti o Waitangi:
|
||||
|
||||
**Sovereignty**: You control decisions affecting your data and values. No imposed hierarchies.
|
||||
|
||||
**Transparency**: Every governance decision is visible and auditable.
|
||||
|
||||
**Harmlessness**: When AI is uncertain, it asks—architectural constraints prevent failures.
|
||||
|
||||
**Community**: Open-source and accessible. Collective work, not competitive advantage.
|
||||
|
||||
We learned from Te Tiriti: Multiple valid value frameworks can coexist without hierarchy. When efficiency conflicts with safety—humans navigate trade-offs, AI enforces decisions.
|
||||
|
||||
https://agenticgovernance.digital/about/values.html
|
||||
|
||||
---
|
||||
|
||||
**Recommended**: Version 1 for general audience, Version 2 for cross-posting, Version 3 for NZ focus
|
||||
BIN
docs/outreach/Modern T Logo with Colourful Circles.png
Normal file
BIN
docs/outreach/Modern T Logo with Colourful Circles.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 1 MiB |
521
docs/outreach/PHASE-0-ARTICLE-CONCEPT-VALIDATION.md
Normal file
521
docs/outreach/PHASE-0-ARTICLE-CONCEPT-VALIDATION.md
Normal file
|
|
@ -0,0 +1,521 @@
|
|||
# Phase 0: Article Concept Validation Plan
|
||||
|
||||
**Purpose**: Validate article angles with 5-10 aligned individuals BEFORE writing full articles
|
||||
**Method**: Send concept descriptions + evaluation structure, gather feedback
|
||||
**Goal**: Understand what resonates, what's missing, what needs refinement
|
||||
**Date**: 30 October 2025
|
||||
|
||||
---
|
||||
|
||||
## Contact Profiles & Article Matching
|
||||
|
||||
### Profile 1: AI Forum NZ Member (Tech Policy/Governance)
|
||||
|
||||
**Background**:
|
||||
- Member of AI Forum New Zealand (aiforum.org.nz)
|
||||
- Cross-sector perspective (business, academia, government, public interest)
|
||||
- Concerned with "prosperous, inclusive and equitable future Aotearoa"
|
||||
- Likely involved in AI governance working groups or policy discussions
|
||||
- Values: Responsible AI, NZ-specific context, practical implementation
|
||||
|
||||
**Primary Interests**: Governance mechanisms that work in NZ context, plural values (Māori/Pākehā/Pacific perspectives), avoiding extractive big tech approaches
|
||||
|
||||
**Article Versions to Test**:
|
||||
- **PRIMARY**: Version D (Governance Mechanisms for Plural Moral Values - Aotearoa angle)
|
||||
- **SECONDARY**: Version A (Amoral AI to Plural Moral Values - organizational lens)
|
||||
- **TERTIARY**: Version E (Governance Mechanism Gap - comprehensive)
|
||||
|
||||
**Key Validation Questions**:
|
||||
- Does "plural moral values" framing resonate in Aotearoa context?
|
||||
- Is Te Tiriti reference authentic or appropriative?
|
||||
- What governance challenges do NZ organizations face that aren't addressed?
|
||||
|
||||
---
|
||||
|
||||
### Profile 2: Retired World Bank Legal Department Member
|
||||
|
||||
**Background**:
|
||||
- Decades of experience in international development law
|
||||
- Dealt with governance frameworks across vastly different cultural/legal contexts
|
||||
- Deep understanding of what makes governance "on paper" vs. "in practice"
|
||||
- Seen countless governance mechanisms fail due to implementation gaps
|
||||
- Values: Evidence-based governance, cross-cultural applicability, institutional rigor
|
||||
|
||||
**Primary Interests**: Whether architectural enforcement could address governance theatre problems they've witnessed, applicability across jurisdictions, regulatory credibility
|
||||
|
||||
**Article Versions to Test**:
|
||||
- **PRIMARY**: Version B (GDPR/Compliance - architectural approach)
|
||||
- **SECONDARY**: Version A (Plural Moral Values - governance lens)
|
||||
- **TERTIARY**: Version E (Governance Mechanism Gap - comprehensive)
|
||||
|
||||
**Key Validation Questions**:
|
||||
- Does "governance theatre vs. enforcement" distinction ring true?
|
||||
- Would regulators in different jurisdictions find architectural approach credible?
|
||||
- What governance failures have they seen that this might address/miss?
|
||||
|
||||
---
|
||||
|
||||
### Profile 3: Tech-Savvy Modern Developer (Medium Software Company, Australia)
|
||||
|
||||
**Background**:
|
||||
- Works for medium-sized software development/tech support company
|
||||
- Implements AI features in production systems
|
||||
- Faces practical challenges: context limits, API costs, deployment complexity
|
||||
- Likely dealing with: LLM integration, prompt engineering, production failures
|
||||
- Values: What actually works, technical honesty, avoiding vendor hype
|
||||
|
||||
**Primary Interests**: Whether architectural constraints solve real problems they're facing, technical feasibility, overhead costs, production reliability
|
||||
|
||||
**Article Versions to Test**:
|
||||
- **PRIMARY**: Version C (Architectural Constraints vs. Behavioral Training)
|
||||
- **SECONDARY**: Version E (Governance Mechanism Gap - technical sections)
|
||||
- **TERTIARY**: Version A (Amoral AI problem - if they're seeing this)
|
||||
|
||||
**Key Validation Questions**:
|
||||
- Are they experiencing "pattern recognition overrides instructions" failures?
|
||||
- Does "more training prolongs the pain" match their experience?
|
||||
- What technical governance challenges aren't addressed?
|
||||
|
||||
---
|
||||
|
||||
### Profile 4: Video Content Creator Company Principal (Small Business, AI-Powered)
|
||||
|
||||
**Background**:
|
||||
- Runs small video content creation company
|
||||
- Uses AI in production workflow (editing, generation, marketing)
|
||||
- Serves clients in publicity/marketing sectors
|
||||
- Faces: Client confidentiality, brand voice consistency, quality control
|
||||
- Values: Practical tools, client trust, competitive differentiation
|
||||
|
||||
**Primary Interests**: Whether governance helps maintain quality/trust without slowing production, client data protection, brand alignment
|
||||
|
||||
**Article Versions to Test**:
|
||||
- **PRIMARY**: Version B (GDPR/Compliance - client data protection angle)
|
||||
- **SECONDARY**: Version A (Organizational judgment - maintaining quality)
|
||||
- **TERTIARY**: Version E (Governance Gap - small business practicality)
|
||||
|
||||
**Key Validation Questions**:
|
||||
- Does "governance mechanism gap" resonate for small business context?
|
||||
- What governance challenges do they face that large enterprises don't?
|
||||
- Is architectural approach overkill for their scale, or exactly what's needed?
|
||||
|
||||
---
|
||||
|
||||
### Profile 5: SVP, Deputy General Counsel, Chief AI Governance & Privacy Officer (Large Corporate, 70k+ employees)
|
||||
|
||||
**Background**:
|
||||
- C-suite legal/governance role at large organization
|
||||
- Responsible for AI governance strategy across entire enterprise
|
||||
- Faces: Board oversight, regulatory compliance (GDPR/CCPA/SOC2), incident response
|
||||
- Deals with: Multiple business units, varied use cases, audit requirements
|
||||
- Values: Defensible governance, audit trails, regulatory credibility, scalability
|
||||
|
||||
**Primary Interests**: Whether architectural approach provides audit-grade evidence, scales across organization, satisfies regulators, handles incident investigations
|
||||
|
||||
**Article Versions to Test**:
|
||||
- **PRIMARY**: Version B (GDPR - architectural compliance)
|
||||
- **SECONDARY**: Version A (Organizational Hollowing - executive lens)
|
||||
- **TERTIARY**: Version E (Governance Mechanism Gap - comprehensive)
|
||||
|
||||
**Key Validation Questions**:
|
||||
- Would this satisfy regulators/auditors they work with?
|
||||
- Does "architectural enforcement vs. policy compliance" distinction land?
|
||||
- What governance evidence gaps do they currently face?
|
||||
|
||||
---
|
||||
|
||||
### Profile 6: Retired World Bank Infrastructure Consultant (40+ Years, Global Projects)
|
||||
|
||||
**Background**:
|
||||
- Consulted on hundreds of large-scale infrastructure projects globally
|
||||
- Witnessed governance successes/failures across cultures and contexts
|
||||
- Deep understanding of: What looks good on paper vs. works in field
|
||||
- Seen: Governance theatre, implementation challenges, cultural adaptation
|
||||
- Values: Pragmatic governance, cross-cultural effectiveness, institutional learning
|
||||
|
||||
**Primary Interests**: Whether "one approach" positioning is appropriate, cross-cultural applicability, organizational capacity requirements, implementation realism
|
||||
|
||||
**Article Versions to Test**:
|
||||
- **PRIMARY**: Version A (Plural Moral Values - cross-cultural governance)
|
||||
- **SECONDARY**: Version D (Aotearoa angle - governance in multicultural context)
|
||||
- **TERTIARY**: Version E (Comprehensive - full governance picture)
|
||||
|
||||
**Key Validation Questions**:
|
||||
- Does plural moral values framing apply across cultural contexts they've worked in?
|
||||
- What governance implementation gaps might this approach face?
|
||||
- Is "honest uncertainty" positioning appropriate or undermining?
|
||||
|
||||
---
|
||||
|
||||
## Additional Profiles to Fill Gaps
|
||||
|
||||
### Profile 7: Academic Researcher (AI Ethics/Safety)
|
||||
|
||||
**Background**:
|
||||
- University researcher in AI ethics, safety, or alignment
|
||||
- Publishes in academic venues (FAccT, AIES, AI & Society)
|
||||
- Concerned with: Theoretical rigor, empirical validation, ethical frameworks
|
||||
- Skeptical of: Industry hype, "solutions" without evidence, oversimplification
|
||||
- Values: Research methodology, falsifiability, intellectual honesty
|
||||
|
||||
**Primary Interests**: Whether approach has theoretical grounding, what empirical validation exists, research collaboration opportunities
|
||||
|
||||
**Article Versions to Test**:
|
||||
- **PRIMARY**: Version C (Technical depth - architectural approach)
|
||||
- **SECONDARY**: Version E (Comprehensive - includes research foundations)
|
||||
- **TERTIARY**: Version A (Plural values - theoretical framing)
|
||||
|
||||
**Key Validation Questions**:
|
||||
- Is theoretical framing sound (plural values, incommensurability)?
|
||||
- What empirical evidence would validate/refute this approach?
|
||||
- What research questions does this raise?
|
||||
|
||||
**Gap Filled**: Academic/research perspective, theoretical validation needs
|
||||
|
||||
---
|
||||
|
||||
### Profile 8: Healthcare/Public Sector CIO (High-Stakes AI Deployment)
|
||||
|
||||
**Background**:
|
||||
- CIO or senior IT leader in healthcare, government, or high-stakes public sector
|
||||
- Deploying AI in contexts where failures have severe consequences
|
||||
- Faces: Patient safety, equity concerns, public accountability, resource constraints
|
||||
- Must balance: Innovation pressure vs. risk management
|
||||
- Values: Safety-first, equity, public trust, evidence-based decisions
|
||||
|
||||
**Primary Interests**: Whether governance prevents harm in high-stakes contexts, equity implications, public accountability mechanisms
|
||||
|
||||
**Article Versions to Test**:
|
||||
- **PRIMARY**: Version B (Compliance/Safety - architectural prevention)
|
||||
- **SECONDARY**: Version A (Organizational judgment - high-stakes decisions)
|
||||
- **TERTIARY**: Version D (Plural values - equity/inclusion angle)
|
||||
|
||||
**Key Validation Questions**:
|
||||
- Does this address safety/equity concerns in high-stakes deployments?
|
||||
- What harm scenarios might this miss?
|
||||
- How does this maintain public accountability?
|
||||
|
||||
**Gap Filled**: High-stakes public sector, safety-critical contexts, equity concerns
|
||||
|
||||
---
|
||||
|
||||
## Meta-Validation Letter Template
|
||||
|
||||
**Subject**: AI Governance Article Concepts - Seeking Your Perspective
|
||||
|
||||
Dear [Name],
|
||||
|
||||
I'm reaching out because [specific reason - your work on X / our conversation about Y / you've navigated Z challenges] suggests you might have valuable perspective on a governance problem I'm exploring.
|
||||
|
||||
**Context**: I've been working on architectural approaches to AI governance and am preparing to publish several articles exploring different angles of what I'm calling the "governance mechanism gap." Before investing in writing full articles, I want to validate whether these concepts resonate with people who've actually dealt with governance challenges in practice.
|
||||
|
||||
**Why You**: [Personalized - your experience with plural values in Te Tiriti context / your decades seeing governance theatre in infrastructure projects / your role governing AI at enterprise scale]
|
||||
|
||||
**What I'm Asking**: 10-15 minutes to review brief article concept descriptions and tell me:
|
||||
1. Which angles resonate with challenges you've seen
|
||||
2. What's missing or misframed
|
||||
3. Which concepts would be most valuable for people in your field
|
||||
|
||||
This is validation, not sales. I'm genuinely trying to understand if these framings land before committing to writing and submitting to publications.
|
||||
|
||||
**What You'll Review**:
|
||||
- 5 article concepts (200-300 words each describing angle/thesis)
|
||||
- Simple evaluation structure to guide feedback
|
||||
- Estimated 10-15 minutes total
|
||||
|
||||
**Important**: I'm testing whether the concepts are sound, not asking you to validate technical implementation or endorse the work. Critical feedback is more valuable than agreement.
|
||||
|
||||
If you have time and interest, I'll send the article concepts and evaluation structure. If not, no problem at all.
|
||||
|
||||
Best regards,
|
||||
[Your name]
|
||||
|
||||
P.S. The approach I'm testing is called "Tractatus" - architectural constraints for AI governance with focus on plural moral values. Website (if curious): https://agenticgovernance.digital
|
||||
|
||||
---
|
||||
|
||||
## Article Concept Summaries (For Validation)
|
||||
|
||||
### Version A: From Amoral AI to Plural Moral Values
|
||||
|
||||
**Target Audience**: Culture-conscious leaders (HBR, MIT Sloan, Economist)
|
||||
**Word Count**: 800-950 words
|
||||
**Core Angle**: Organizational hollowing & judgment atrophy
|
||||
|
||||
**The Concept**:
|
||||
|
||||
Organizations are deploying AI agents making thousands of decisions daily with no moral framework—just pattern recognition. Not "making better decisions" but "replacing contextual judgment with amoral intelligence." This creates judgment atrophy: Teams lose capacity for nuanced decisions because AI handles volume.
|
||||
|
||||
The governance gap: Current approaches (policies, training, alignment) hope AI "behaves correctly" but provide no mechanisms to enforce value-aligned decisions before execution. No way to handle incommensurable value conflicts (privacy vs. utility, efficiency vs. equity) without reducing to single-metric optimization.
|
||||
|
||||
One architectural approach exists: Six governance services that enforce plural moral values through structural constraints, not behavioral training. Organizations configure their own value frameworks; architecture ensures AI can't execute value-sensitive decisions without human approval.
|
||||
|
||||
Honest uncertainty: Early evidence from controlled deployment suggests this prevents pattern bias incidents and maintains audit trails. But validation beyond single-project context is ongoing.
|
||||
|
||||
**Key Questions**:
|
||||
- Does "judgment atrophy" resonate with your organizational experience?
|
||||
- Is "amoral AI" (as problem framing) accurate to what you're seeing?
|
||||
- What's missing in how this describes the governance challenge?
|
||||
|
||||
---
|
||||
|
||||
### Version B: How AI Governance Prevents GDPR Violations
|
||||
|
||||
**Target Audience**: GDPR compliance officers, risk management (FT, WSJ)
|
||||
**Word Count**: 800-950 words
|
||||
**Core Angle**: Architectural compliance vs. policy compliance
|
||||
|
||||
**The Concept**:
|
||||
|
||||
Your AI just exposed customer PII in a log file. €20M GDPR fine. Auditor asks: "How did you prevent this?" Answer: "We told the AI not to." That's not compliance evidence—that's hope-based governance.
|
||||
|
||||
The compliance gap: GDPR Article 25 requires "data protection by design"—technical safeguards, not just policies. But current approaches rely on training AI to "respect privacy" or prompting it to "check for PII." No architectural enforcement, no audit trail showing prevention occurred.
|
||||
|
||||
One architectural approach: BoundaryEnforcer service blocks AI actions that violate stored privacy rules before execution. CrossReferenceValidator checks every database query against PII exposure rules. Audit logs provide compliance evidence: "On [date], system blocked AI from including PII in response, human reviewed context, approved redacted version."
|
||||
|
||||
This addresses value conflicts: Privacy vs. data utility are incommensurable—can't train AI to "balance" them. Architecture forces explicit human decision on trade-offs.
|
||||
|
||||
Honest uncertainty: We think this could satisfy GDPR Article 25 requirements, but regulatory validation is ongoing. Deploying in controlled context, gathering evidence.
|
||||
|
||||
**Key Questions**:
|
||||
- Would this audit trail satisfy regulators you work with?
|
||||
- Does "architectural enforcement" make sense for your compliance context?
|
||||
- What GDPR challenges does this miss?
|
||||
|
||||
---
|
||||
|
||||
### Version C: Architectural Constraints vs. Behavioral Training
|
||||
|
||||
**Target Audience**: Technologists, production engineers (IEEE Spectrum, ACM Queue)
|
||||
**Word Count**: 1000-1500 words
|
||||
**Core Angle**: Why hope-based governance fails at scale
|
||||
|
||||
**The Concept**:
|
||||
|
||||
You trained your AI on 10,000 examples of "good decisions." In production, it overrides human instructions when pattern recognition triggers faster than instruction-following. You add more training. Override rate increases. "More training prolongs the pain."
|
||||
|
||||
The technical problem: Behavioral approaches (RLHF, Constitutional AI, prompt engineering) shape tendencies at model level. Failures happen at deployment level under context pressure. Training is probabilistic; governance requires deterministic. Training degrades under novel contexts; architecture maintains.
|
||||
|
||||
Example: 27027 Incident. User: "Use MongoDB port [custom-port]." Instruction stored (HIGH persistence). Session reaches 107k tokens (53.5% context pressure). AI attempts connection to default port (from training). Pattern recognition dominated over explicit instruction.
|
||||
|
||||
Architectural solution: CrossReferenceValidator checks attempted action (default port) against stored instruction (custom port). Detects conflict. Blocks before execution. Audit log documents prevention.
|
||||
|
||||
Six services enforce architecturally: BoundaryEnforcer (values decisions), CrossReferenceValidator (instruction conflicts), MetacognitiveVerifier (reasoning quality), ContextPressureMonitor (degradation detection), InstructionPersistenceClassifier (institutional memory), PluralisticDeliberationOrchestrator (value conflicts).
|
||||
|
||||
Honest uncertainty: Works in controlled deployment. Scales to production? Finding out.
|
||||
|
||||
**Key Questions**:
|
||||
- Are you seeing "pattern overrides instruction" failures?
|
||||
- Does architectural vs. behavioral distinction make technical sense?
|
||||
- What failure modes does this approach miss?
|
||||
|
||||
---
|
||||
|
||||
### Version D: Governance Mechanisms for Plural Moral Values (Aotearoa)
|
||||
|
||||
**Target Audience**: Culture-conscious leaders (NZ/Pacific context)
|
||||
**Word Count**: 600-800 words
|
||||
**Core Angle**: Learning from Te Tiriti governance model
|
||||
|
||||
**The Concept**:
|
||||
|
||||
Aotearoa has something to teach the world about governing systems where multiple valid value frameworks must coexist: Te Tiriti o Waitangi. Not a hierarchy of values (Pākehā over Māori or vice versa), but mechanisms for plural moral values to navigate conflicts through relationship and process.
|
||||
|
||||
Now we're deploying AI systems facing the same governance challenge: Efficiency vs. equity, privacy vs. utility, innovation vs. safety—incommensurable values that can't be reduced to single metrics. Current AI governance attempts value hierarchy ("privacy first" or "efficiency paramount") or hopes AI will "balance" them.
|
||||
|
||||
One architectural approach learns from Te Tiriti model: Create mechanisms for plural values to coexist, surface conflicts explicitly, require human decision on trade-offs. Organizations configure their own value frameworks; architecture ensures conflicts get human judgment.
|
||||
|
||||
What's at stake for Aotearoa: Either lead on governance innovation (small nations can move faster than big tech), or import extractive big tech governance that ignores Te Tiriti principles.
|
||||
|
||||
Honest uncertainty: Testing in controlled context. Does this model apply beyond Aotearoa? Finding out.
|
||||
|
||||
**Key Questions**:
|
||||
- Is Te Tiriti reference authentic or appropriative?
|
||||
- Does "plural moral values" framing apply to challenges you see?
|
||||
- What governance opportunities/risks does Aotearoa face with AI?
|
||||
|
||||
---
|
||||
|
||||
### Version E: The Governance Mechanism Gap
|
||||
|
||||
**Target Audience**: Mixed (Substack, Medium, LinkedIn)
|
||||
**Word Count**: 1500-2000 words
|
||||
**Core Angle**: Comprehensive exploration of all angles
|
||||
|
||||
**The Concept**:
|
||||
|
||||
Your best decisions come from contextual judgment—the "je ne sais quoi" distinguishing great from merely okay. Now you're deploying AI making thousands of decisions daily. Pattern recognition, not contextual judgment. Amoral intelligence making calls that should involve moral frameworks.
|
||||
|
||||
The governance mechanism gap: Current approaches hope AI "behaves correctly" through training, policies, or alignment. No mechanisms to:
|
||||
- Detect when AI makes values-sensitive decisions
|
||||
- Surface incommensurable value conflicts
|
||||
- Enforce human judgment on trade-offs
|
||||
- Maintain audit trails for regulators
|
||||
- Prevent judgment atrophy in human teams
|
||||
|
||||
One architectural approach: Six services providing governance mechanisms. Not training AI to "be moral"—architecting systems so AI can't execute value-sensitive decisions without human approval.
|
||||
|
||||
What's at stake: Organizational hollowing. Teams lose judgment capacity when AI handles volume without mechanisms to preserve human decision-making on what matters. Tacit knowledge stops transferring. Resilience traded for efficiency.
|
||||
|
||||
Unexpected early evidence: In controlled deployment, prevented pattern bias incidents, maintained instruction persistence under context pressure, generated audit trails regulators found credible. But this is single-project context—broader validation ongoing.
|
||||
|
||||
Honest uncertainty: We think architectural enforcement works at scale, but we're finding out. This is one approach among possible others.
|
||||
|
||||
**Key Questions**:
|
||||
- Does "governance mechanism gap" describe challenges you're experiencing?
|
||||
- Which sections resonate most (technical/organizational/compliance)?
|
||||
- What's missing from this framing?
|
||||
|
||||
---
|
||||
|
||||
## Evaluation Structure (Reply Template)
|
||||
|
||||
**Instructions**: Please rate each section and provide brief comments. Feel free to skip sections that aren't relevant to your context.
|
||||
|
||||
### Part 1: Problem Framing (Rate 1-5, 5=Strongly Resonates)
|
||||
|
||||
**A. "Governance Mechanism Gap"**
|
||||
Does this describe a real problem you've seen?
|
||||
Rating: [ 1 / 2 / 3 / 4 / 5 ]
|
||||
Comment:
|
||||
|
||||
**B. "Amoral AI"** (AI with no moral framework, just pattern recognition)
|
||||
Accurate description of current AI systems?
|
||||
Rating: [ 1 / 2 / 3 / 4 / 5 ]
|
||||
Comment:
|
||||
|
||||
**C. "Judgment Atrophy"** (Organizational capacity for contextual decisions degrades)
|
||||
Seeing this in your organization/field?
|
||||
Rating: [ 1 / 2 / 3 / 4 / 5 ]
|
||||
Comment:
|
||||
|
||||
**D. "Hope-Based Governance"** (Policies/training without enforcement mechanisms)
|
||||
Does this describe current approaches?
|
||||
Rating: [ 1 / 2 / 3 / 4 / 5 ]
|
||||
Comment:
|
||||
|
||||
---
|
||||
|
||||
### Part 2: Solution Framing (Rate 1-5, 5=Strongly Resonates)
|
||||
|
||||
**E. "Architectural Constraints vs. Behavioral Training"**
|
||||
Does this distinction make sense?
|
||||
Rating: [ 1 / 2 / 3 / 4 / 5 ]
|
||||
Comment:
|
||||
|
||||
**F. "Plural Moral Values"** (Organizations navigate own value conflicts, not imposed hierarchy)
|
||||
Resonates with governance challenges you face?
|
||||
Rating: [ 1 / 2 / 3 / 4 / 5 ]
|
||||
Comment:
|
||||
|
||||
**G. "Incommensurable Values"** (Privacy vs. utility can't be reduced to single metric)
|
||||
Matches your experience?
|
||||
Rating: [ 1 / 2 / 3 / 4 / 5 ]
|
||||
Comment:
|
||||
|
||||
**H. "Honest Uncertainty"** (We think this works, but we're finding out)
|
||||
Appropriate positioning or undermining credibility?
|
||||
Rating: [ 1 / 2 / 3 / 4 / 5 ]
|
||||
Comment:
|
||||
|
||||
---
|
||||
|
||||
### Part 3: Article Concepts (Which would be most valuable?)
|
||||
|
||||
**Which article version(s) would be most relevant for people in your field?**
|
||||
[ ] Version A: Organizational Hollowing (HBR/MIT Sloan)
|
||||
[ ] Version B: GDPR/Compliance (FT/WSJ)
|
||||
[ ] Version C: Technical Depth (IEEE/ACM)
|
||||
[ ] Version D: Aotearoa Governance (NZ/Pacific)
|
||||
[ ] Version E: Comprehensive (Substack/Medium)
|
||||
|
||||
**Why?**
|
||||
|
||||
---
|
||||
|
||||
### Part 4: What's Missing?
|
||||
|
||||
**A. What governance challenges does this framing NOT address?**
|
||||
|
||||
**B. What angles or examples would strengthen these concepts?**
|
||||
|
||||
**C. Who else should read these articles? (Roles/industries)**
|
||||
|
||||
---
|
||||
|
||||
### Part 5: Critical Feedback
|
||||
|
||||
**What concerns or red flags do you see with this approach?**
|
||||
|
||||
---
|
||||
|
||||
### Part 6: Would You Read It?
|
||||
|
||||
If published in [relevant venue for your profile], would you:
|
||||
[ ] Definitely read
|
||||
[ ] Probably read
|
||||
[ ] Maybe read
|
||||
[ ] Probably not read
|
||||
[ ] Definitely not read
|
||||
|
||||
**Why?**
|
||||
|
||||
---
|
||||
|
||||
**Thank you for your time and perspective!**
|
||||
|
||||
Please return this evaluation via email or we can discuss over a call if you prefer.
|
||||
|
||||
---
|
||||
|
||||
## Profile → Article → Evaluation Mapping
|
||||
|
||||
| Profile | Primary Article | Secondary | Key Evaluation Focus |
|
||||
|---------|----------------|-----------|---------------------|
|
||||
| 1. AI Forum NZ | Version D (Aotearoa) | A, E | Te Tiriti authenticity, plural values framing, NZ context |
|
||||
| 2. World Bank Legal | Version B (GDPR) | A, E | Governance theatre vs. enforcement, cross-jurisdictional applicability |
|
||||
| 3. Tech Developer | Version C (Technical) | E, A | Pattern override failures, architectural feasibility, overhead |
|
||||
| 4. Video Creator | Version B (GDPR) | A, E | Small business practicality, client data protection |
|
||||
| 5. Chief AI Officer | Version B (GDPR) | A, E | Audit credibility, regulatory satisfaction, scalability |
|
||||
| 6. Infrastructure Consultant | Version A (Plural Values) | D, E | Cross-cultural applicability, implementation realism |
|
||||
| 7. Academic Researcher | Version C (Technical) | E, A | Theoretical rigor, empirical validation, research questions |
|
||||
| 8. Healthcare CIO | Version B (GDPR/Safety) | A, D | Safety/equity, harm prevention, public accountability |
|
||||
|
||||
---
|
||||
|
||||
## Success Criteria (Phase 0 Validation)
|
||||
|
||||
**Minimum Success** (3-5 responses):
|
||||
- At least 3 contacts provide feedback
|
||||
- Average ratings >3.0 on problem framing sections
|
||||
- Identify 2-3 missing angles to incorporate
|
||||
- Validate 1-2 article concepts are worth writing fully
|
||||
|
||||
**Strong Success** (5-10 responses):
|
||||
- 5+ contacts provide detailed feedback
|
||||
- Average ratings >3.5 on both problem and solution framing
|
||||
- Consistent themes in "what's missing"
|
||||
- Clear indication of which articles to prioritize
|
||||
- 1-2 contacts want to continue dialogue
|
||||
|
||||
**Pivot Triggers**:
|
||||
- Average ratings <2.5 = Major reframing needed
|
||||
- "What's missing" reveals fundamental blind spots = Pause and research
|
||||
- Multiple "would not read" responses = Wrong target audience or framing
|
||||
- Concerns about "honest uncertainty" undermining credibility = Reconsider positioning
|
||||
|
||||
---
|
||||
|
||||
**Next Steps After Validation**:
|
||||
1. Analyze feedback patterns across profiles
|
||||
2. Identify strongest article concepts (priority order)
|
||||
3. Incorporate missing angles and strengthen weak framings
|
||||
4. Write full versions of top 2-3 articles
|
||||
5. Proceed to Phase 1 (Low-Risk Social Exposure)
|
||||
|
||||
---
|
||||
|
||||
**Status**: Ready for deployment
|
||||
**Estimated Time per Contact**: 10-15 minutes
|
||||
**Total Validation Window**: 1-2 weeks
|
||||
**Decision Point**: Week 2 - Analyze feedback, decide which articles to write
|
||||
302
docs/outreach/PHASE-0-FEEDBACK-COLLECTION.md
Normal file
302
docs/outreach/PHASE-0-FEEDBACK-COLLECTION.md
Normal file
|
|
@ -0,0 +1,302 @@
|
|||
# Phase 0 Feedback Collection System
|
||||
|
||||
**Goal**: Make it easy for validation contacts to share feedback
|
||||
**Principle**: Low friction, multiple channels, qualitative over quantitative
|
||||
|
||||
---
|
||||
|
||||
## 📊 Feedback Collection Methods
|
||||
|
||||
### Method 1: Email Responses (Recommended - Lowest Friction)
|
||||
|
||||
**Why**: Personal, conversational, preserves context
|
||||
**Setup**: None required
|
||||
**Process**:
|
||||
1. Validation contacts reply directly to your outreach email
|
||||
2. Copy key insights into PHASE-0-VALIDATION-TRACKER.md
|
||||
3. Maintain personal dialogue thread
|
||||
|
||||
**Pros**:
|
||||
- ✅ Zero barrier to feedback
|
||||
- ✅ Allows follow-up questions
|
||||
- ✅ Builds relationships
|
||||
- ✅ Captures nuance/context
|
||||
|
||||
**Cons**:
|
||||
- ❌ Manual tracking required
|
||||
- ❌ Not structured
|
||||
|
||||
---
|
||||
|
||||
### Method 2: Substack Comments
|
||||
|
||||
**Why**: Public feedback visible to others, builds community
|
||||
**Setup**: Already enabled on your Substack
|
||||
**Process**:
|
||||
1. Validation contacts comment on article
|
||||
2. Respond directly in comments
|
||||
3. Copy key insights to tracker
|
||||
|
||||
**Pros**:
|
||||
- ✅ Public dialogue
|
||||
- ✅ Other readers see feedback
|
||||
- ✅ Low friction
|
||||
|
||||
**Cons**:
|
||||
- ❌ Less detailed than private feedback
|
||||
- ❌ Some won't comment publicly
|
||||
|
||||
---
|
||||
|
||||
### Method 3: Dedicated Feedback Page (Website)
|
||||
|
||||
**Why**: Centralized, structured, professional
|
||||
**Setup**: Create simple feedback form on agenticgovernance.digital
|
||||
**Process**:
|
||||
1. Add route: /feedback or /phase-0-feedback
|
||||
2. Simple form: Name, Email, Feedback text
|
||||
3. Submit → saves to MongoDB or emails you
|
||||
|
||||
**Questions to include**:
|
||||
- What's your role? (Researcher / Implementer / Leader / Other)
|
||||
- Does "governance mechanism gap" resonate with your experience?
|
||||
- What sections were most/least clear?
|
||||
- What questions does this raise?
|
||||
- Would you recommend this to someone in your field?
|
||||
- Open feedback
|
||||
|
||||
**Pros**:
|
||||
- ✅ Structured data
|
||||
- ✅ Professional
|
||||
- ✅ Easy to share link
|
||||
|
||||
**Cons**:
|
||||
- ❌ Requires development work
|
||||
- ❌ Form friction (vs. just replying to email)
|
||||
|
||||
---
|
||||
|
||||
### Method 4: LinkedIn Messages
|
||||
|
||||
**Why**: Where professional conversations happen
|
||||
**Setup**: None required
|
||||
**Process**:
|
||||
1. Contacts message you on LinkedIn
|
||||
2. Copy insights to tracker
|
||||
3. Continue dialogue
|
||||
|
||||
**Pros**:
|
||||
- ✅ Platform they already use
|
||||
- ✅ Low friction
|
||||
- ✅ Networking benefit
|
||||
|
||||
**Cons**:
|
||||
- ❌ Manual tracking
|
||||
- ❌ Can get lost in LinkedIn noise
|
||||
|
||||
---
|
||||
|
||||
### Method 5: Scheduled Calls (Optional)
|
||||
|
||||
**Why**: Deep dive, nuanced feedback
|
||||
**Setup**: Calendly or manual scheduling
|
||||
**Process**:
|
||||
1. Offer 20-minute call to interested validation contacts
|
||||
2. Prepare questions (see below)
|
||||
3. Take notes during call
|
||||
4. Document in tracker
|
||||
|
||||
**When to use**: If someone shows deep interest or raises complex questions
|
||||
|
||||
**Pros**:
|
||||
- ✅ Richest feedback
|
||||
- ✅ Relationship building
|
||||
- ✅ Can explore edge cases
|
||||
|
||||
**Cons**:
|
||||
- ❌ Time intensive
|
||||
- ❌ Doesn't scale
|
||||
- ❌ Can feel like "sales call" if not framed carefully
|
||||
|
||||
---
|
||||
|
||||
## 🎯 Recommended Approach (Phase 0)
|
||||
|
||||
**Primary**: Email responses
|
||||
**Secondary**: Substack comments
|
||||
**Tertiary**: LinkedIn messages
|
||||
|
||||
**Rationale**: Keep it simple. Phase 0 is 5-10 people. Personal dialogue > structured data.
|
||||
|
||||
---
|
||||
|
||||
## 💬 Key Feedback Questions
|
||||
|
||||
When collecting feedback (email, call, or in-person), explore:
|
||||
|
||||
### Resonance
|
||||
- Does "governance mechanism gap" match your experience?
|
||||
- Have you seen "judgment atrophy" in organizations deploying AI?
|
||||
- Does the "amoral AI" framing make sense?
|
||||
|
||||
### Technical Validity
|
||||
- Are the six services architecturally sound?
|
||||
- What blind spots do you see in this approach?
|
||||
- Where would this break in your context?
|
||||
|
||||
### Messaging Clarity
|
||||
- What sections were confusing?
|
||||
- What examples resonated most?
|
||||
- What would you change about how this is explained?
|
||||
|
||||
### Audience Fit
|
||||
- Would you share this with someone in your field?
|
||||
- Who is this most relevant for?
|
||||
- What's missing for [researchers/implementers/leaders]?
|
||||
|
||||
### Open-Ended
|
||||
- What questions does this raise for you?
|
||||
- What would you want to know before recommending this?
|
||||
- What does this remind you of (similar work/failures)?
|
||||
|
||||
---
|
||||
|
||||
## 📝 Documenting Feedback
|
||||
|
||||
After each feedback conversation/email:
|
||||
|
||||
### 1. Update Tracker
|
||||
Open: `docs/outreach/PHASE-0-VALIDATION-TRACKER.md`
|
||||
|
||||
Fill in:
|
||||
- Response summary
|
||||
- Key insights
|
||||
- Status update
|
||||
|
||||
### 2. Extract Patterns
|
||||
As feedback accumulates, look for:
|
||||
- **Common confusion points** (need clarification)
|
||||
- **Repeated "aha moments"** (what resonates)
|
||||
- **Blind spots identified** (technical/conceptual gaps)
|
||||
- **Unexpected questions** (what you didn't anticipate)
|
||||
|
||||
### 3. Update Learnings Section
|
||||
In tracker under "Key Learnings":
|
||||
- What's working
|
||||
- What needs refinement
|
||||
- Unexpected insights
|
||||
|
||||
---
|
||||
|
||||
## 🔄 Weekly Review Process
|
||||
|
||||
**Every Monday** (or set day), review feedback:
|
||||
|
||||
### Week 1 Check-In (After 5-7 days)
|
||||
- How many contacts have responded?
|
||||
- What patterns are emerging?
|
||||
- Is messaging clear or confusing?
|
||||
- Ready to refine or keep gathering feedback?
|
||||
|
||||
### Week 2 Check-In
|
||||
- Have you reached 5+ validation contacts?
|
||||
- Is core thesis validated or challenged?
|
||||
- What needs to change before Phase 1?
|
||||
|
||||
### Week 3 Check-In
|
||||
- Ready for Phase 1 transition?
|
||||
- Final messaging refinements needed?
|
||||
- Update VERSION-E-SUBSTACK-DRAFT.md if changes required
|
||||
|
||||
### Week 4 Decision Point
|
||||
- Move to Phase 1 (low-risk social exposure)?
|
||||
- Continue Phase 0 with new contacts?
|
||||
- Pivot messaging based on learnings?
|
||||
|
||||
---
|
||||
|
||||
## 📧 Feedback Acknowledgment Template
|
||||
|
||||
When someone provides feedback, acknowledge quickly:
|
||||
|
||||
---
|
||||
|
||||
**Email Subject**: Re: [Their original subject]
|
||||
|
||||
[Name],
|
||||
|
||||
Thank you for taking the time to read and share your thoughts - this is exactly the kind of feedback I need at this validation stage.
|
||||
|
||||
[Address 1-2 specific points they made]
|
||||
|
||||
This helps me understand [what you learned]. [If they raised a question, answer it or acknowledge you need to think more about it]
|
||||
|
||||
I'll keep you posted as this evolves. If you'd like to see how the framework develops, I can add you to Phase 1 updates (or you can subscribe on Substack if you prefer).
|
||||
|
||||
Either way, grateful for your perspective.
|
||||
|
||||
Best,
|
||||
[Your name]
|
||||
|
||||
---
|
||||
|
||||
## ⚠️ Red Flags to Watch For
|
||||
|
||||
If feedback reveals:
|
||||
|
||||
### Technical Red Flags
|
||||
- Multiple people don't understand six services architecture
|
||||
- Implementers see obvious flaws you missed
|
||||
- "This won't work because X" (repeated pattern)
|
||||
|
||||
**Action**: Pause outreach, address technical gaps, refine article
|
||||
|
||||
### Messaging Red Flags
|
||||
- "I don't understand the problem you're solving"
|
||||
- "This sounds like [completely different thing]"
|
||||
- "Is this just [oversimplification of framework]?"
|
||||
|
||||
**Action**: Clarify positioning, refine framing, add examples
|
||||
|
||||
### Audience Fit Red Flags
|
||||
- Researchers don't see research value
|
||||
- Implementers don't see operational relevance
|
||||
- Leaders don't connect to organizational challenges
|
||||
|
||||
**Action**: Re-evaluate target audience or messaging for each audience type
|
||||
|
||||
---
|
||||
|
||||
## ✅ Success Signals
|
||||
|
||||
If feedback shows:
|
||||
|
||||
- "Yes, I see this problem in my organization"
|
||||
- "This matches my research on [related topic]"
|
||||
- "I'd share this with [specific person/role]"
|
||||
- "What would it take to deploy this in [context]?"
|
||||
- Thoughtful questions about implementation/scaling
|
||||
- Unsolicited sharing (they forward to colleagues)
|
||||
|
||||
**Action**: Document patterns, continue Phase 0, prepare for Phase 1
|
||||
|
||||
---
|
||||
|
||||
## 🎯 Phase 0 → Phase 1 Transition Criteria
|
||||
|
||||
**Ready to move to Phase 1 when:**
|
||||
|
||||
- [ ] 5+ validation contacts provided feedback
|
||||
- [ ] Core thesis validated (governance gap recognized)
|
||||
- [ ] No major messaging confusion
|
||||
- [ ] At least 2 contacts said "this matches my experience"
|
||||
- [ ] Technical approach validated by implementers/researchers
|
||||
- [ ] You've refined article based on feedback (if needed)
|
||||
|
||||
**Then proceed to**: Phase 1 (Hacker News, Reddit, LinkedIn, ACM TechNews)
|
||||
|
||||
---
|
||||
|
||||
**Current Status**: Phase 0 Active
|
||||
**Next Review**: [Set date]
|
||||
**Feedback Count**: 0 / 5 minimum
|
||||
220
docs/outreach/PHASE-0-OUTREACH-TEMPLATES.md
Normal file
220
docs/outreach/PHASE-0-OUTREACH-TEMPLATES.md
Normal file
|
|
@ -0,0 +1,220 @@
|
|||
# Phase 0 Validation Outreach Templates
|
||||
|
||||
**Purpose**: Personal validation with 5-10 aligned individuals
|
||||
**Tone**: Honest, direct, seeking genuine feedback (not pitching)
|
||||
**Goal**: Validation, not recruitment
|
||||
|
||||
---
|
||||
|
||||
## 📧 Email Templates
|
||||
|
||||
### Template 1: For Researchers (AI Safety/Alignment)
|
||||
|
||||
**Subject**: Governance mechanism gap in AI deployment - does this match your experience?
|
||||
|
||||
Hi [Name],
|
||||
|
||||
I've been working on a governance framework for AI systems and recently published an article exploring what I'm calling the "governance mechanism gap" - the structural problem that emerges when AI makes thousands of decisions daily with no architecture for moral judgment or value conflicts.
|
||||
|
||||
I'm in Phase 0 validation (honest uncertainty, testing an approach) and you came to mind because [specific reason - your work on X, your experience with Y, our conversation about Z].
|
||||
|
||||
The core question: Can governance for plural moral values work through architectural constraints rather than behavioral training? I've deployed six services (BoundaryEnforcer, CrossReferenceValidator, etc.) in production and am sharing what I'm learning - works, fails, still validating.
|
||||
|
||||
**Article**: [Substack URL]
|
||||
**Framework**: https://agenticgovernance.digital
|
||||
|
||||
**What would help**: Your perspective. Does the "governance mechanism gap" resonate with your research? Do you see blind spots in the architectural approach? What questions would you ask?
|
||||
|
||||
This isn't a pitch - I'm genuinely testing whether this framing makes sense before broader outreach. Your critical feedback is more valuable than agreement.
|
||||
|
||||
If this doesn't interest you, no problem at all. But if it does, I'd value your thoughts.
|
||||
|
||||
Best,
|
||||
[Your name]
|
||||
|
||||
---
|
||||
|
||||
### Template 2: For Implementers (Production AI Systems)
|
||||
|
||||
**Subject**: Architectural constraints for AI governance - testing an approach
|
||||
|
||||
Hi [Name],
|
||||
|
||||
I've been building a governance framework for AI agents and published an article on what I'm seeing: organizations deploying AI at scale hit a "governance mechanism gap" - thousands of amoral decisions daily, no architecture for value conflicts, judgment capacity atrophying.
|
||||
|
||||
This is Phase 0 (honest testing, not proven solution) and you came to mind because [specific reason - you're deploying AI at scale / you've mentioned governance challenges / we discussed this problem].
|
||||
|
||||
The approach: Six services that enforce governance architecturally (BoundaryEnforcer, ContextPressureMonitor, etc.) instead of hoping AI "behaves correctly." Deployed in production for this project. Sharing what works, what fails, what I'm still finding out.
|
||||
|
||||
**Article**: [Substack URL]
|
||||
**Technical docs**: https://agenticgovernance.digital/implementer.html
|
||||
|
||||
**What would help**: Your operational experience. Are you seeing this pattern (judgment atrophy / context pressure / amoral decisions)? Does architectural enforcement make sense for your context? Where would this break?
|
||||
|
||||
Not looking for adoption - looking for validation or refutation from someone in the trenches.
|
||||
|
||||
If you have 10 minutes to read and react, that feedback would be hugely valuable. If not, no worries.
|
||||
|
||||
Cheers,
|
||||
[Your name]
|
||||
|
||||
---
|
||||
|
||||
### Template 3: For Leaders (Organizational Governance)
|
||||
|
||||
**Subject**: Organizational judgment atrophy from AI deployment?
|
||||
|
||||
Hi [Name],
|
||||
|
||||
I've been exploring a governance challenge I keep seeing when organizations deploy AI agents at scale: "judgment atrophy" - contextual decision-making capacity degrades when AI makes thousands of amoral decisions daily.
|
||||
|
||||
I recently published an article on this and thought of you because [specific reason - your leadership in X / our conversation about organizational resilience / you're navigating AI governance].
|
||||
|
||||
The pattern: AI makes 1,000 decisions/day using pattern matching → humans review 10 → "AI decides, we rubber-stamp" → judgment capacity atrophies → tacit knowledge stops transferring → organization becomes brittle.
|
||||
|
||||
I'm testing an architectural approach (Phase 0 validation) that preserves human judgment on value conflicts while scaling AI capability. Six governance services running in production. Honest uncertainty about whether this works beyond single-project context.
|
||||
|
||||
**Article**: [Substack URL]
|
||||
**Framework overview**: https://agenticgovernance.digital/leader.html
|
||||
|
||||
**What would help**: Your perspective. Are you seeing judgment atrophy in your organization? Does "plural moral values" governance make sense for your context? Where would this approach fail?
|
||||
|
||||
This is validation, not sales. Your critical take is what I need to hear before wider outreach.
|
||||
|
||||
If you have time to read and share your thoughts, I'd be grateful. No pressure if not.
|
||||
|
||||
Best regards,
|
||||
[Your name]
|
||||
|
||||
---
|
||||
|
||||
## 💼 LinkedIn Message Templates
|
||||
|
||||
### Template 4: For LinkedIn Connections (General)
|
||||
|
||||
Hi [Name],
|
||||
|
||||
Quick question: Are you seeing "judgment atrophy" where you work? (Contextual decisions increasingly deferred to AI, organizational resilience traded for efficiency?)
|
||||
|
||||
I just published an article exploring this governance gap and testing an architectural approach. Phase 0 validation - not selling, genuinely testing whether this framing resonates.
|
||||
|
||||
[Substack URL]
|
||||
|
||||
Would value your take if you have 10 min. Critical feedback > agreement.
|
||||
|
||||
---
|
||||
|
||||
### Template 5: For Technical LinkedIn Audience
|
||||
|
||||
Hi [Name],
|
||||
|
||||
Testing an approach to AI governance through architectural constraints (not behavioral training). Six services deployed in production - sharing what works, what fails.
|
||||
|
||||
Published Phase 0 article: [Substack URL]
|
||||
Framework: https://agenticgovernance.digital
|
||||
|
||||
Question: Does "governance mechanism gap" match your experience deploying AI systems? Looking for validation/refutation before broader outreach.
|
||||
|
||||
Your technical perspective would be valuable if you have time to read & react.
|
||||
|
||||
---
|
||||
|
||||
### Template 6: For Academic LinkedIn Connections
|
||||
|
||||
Hi [Name],
|
||||
|
||||
Published Phase 0 validation on AI governance approach: architectural constraints for plural moral values.
|
||||
|
||||
Core Q: Can you govern AI without reducing everything to policies/training? Testing six services in production.
|
||||
|
||||
Article: [Substack URL]
|
||||
Research foundations: https://agenticgovernance.digital/researcher.html
|
||||
|
||||
Would value your methodological critique if you have time. Honest uncertainty > certainty claims.
|
||||
|
||||
---
|
||||
|
||||
## 🎯 Direct Message (Slack/Discord/Signal)
|
||||
|
||||
### Template 7: Casual/Direct Format
|
||||
|
||||
Hey [Name],
|
||||
|
||||
Got a sec for a quick question? I just published an article on AI governance (Phase 0 - testing an idea, not selling anything).
|
||||
|
||||
The core: AI makes thousands of amoral decisions daily → no governance mechanisms for value conflicts → organizational judgment atrophy.
|
||||
|
||||
Testing architectural approach (6 services running in production). Want to know if this matches your experience or if I'm seeing patterns that don't exist.
|
||||
|
||||
[Substack URL]
|
||||
|
||||
Honest feedback appreciated if you have 10 min. "This doesn't resonate" is just as useful as "yes, I see this."
|
||||
|
||||
---
|
||||
|
||||
## 📋 Follow-Up Template (If No Response After 7 Days)
|
||||
|
||||
**Subject**: No worries if not interested
|
||||
|
||||
Hi [Name],
|
||||
|
||||
Following up on the AI governance article I sent last week - totally understand if you're swamped or this isn't relevant to your work right now.
|
||||
|
||||
If you do get a chance to read it and have thoughts, I'd still value your perspective. But no pressure at all.
|
||||
|
||||
Thanks,
|
||||
[Your name]
|
||||
|
||||
---
|
||||
|
||||
## 🔄 Response Template (When Feedback Received)
|
||||
|
||||
**Subject**: Re: [Original subject]
|
||||
|
||||
[Name],
|
||||
|
||||
Thank you for this feedback - [specific detail they mentioned] is exactly the kind of insight I need at this validation stage.
|
||||
|
||||
[Address their specific points/questions]
|
||||
|
||||
This helps me understand [what I learned from their feedback]. Would you be open to a brief follow-up conversation if questions emerge during Phase 1, or prefer I keep you posted via updates?
|
||||
|
||||
Either way, grateful for your time and perspective.
|
||||
|
||||
Best,
|
||||
[Your name]
|
||||
|
||||
---
|
||||
|
||||
## 💡 Outreach Best Practices (Phase 0)
|
||||
|
||||
### DO:
|
||||
- ✅ Emphasize validation, not recruitment
|
||||
- ✅ Ask specific questions about their experience
|
||||
- ✅ Be honest about uncertainty
|
||||
- ✅ Make it easy to say "not interested"
|
||||
- ✅ Reference specific context (why them)
|
||||
- ✅ Keep messages short (under 200 words)
|
||||
|
||||
### DON'T:
|
||||
- ❌ Pitch the solution as proven
|
||||
- ❌ Use marketing language ("revolutionary", "game-changing")
|
||||
- ❌ Oversell certainty
|
||||
- ❌ Send mass messages without personalization
|
||||
- ❌ Follow up more than once
|
||||
- ❌ Ask for introductions (Phase 0 is personal validation)
|
||||
|
||||
---
|
||||
|
||||
## 🎯 Personalization Checklist
|
||||
|
||||
Before sending ANY message:
|
||||
- [ ] Specific reason why you're reaching out to THIS person
|
||||
- [ ] Reference to their work/experience/previous conversation
|
||||
- [ ] Tailored question based on their context
|
||||
- [ ] Clear ask (feedback, not adoption)
|
||||
- [ ] Easy out ("no problem if not interested")
|
||||
|
||||
---
|
||||
|
||||
**Remember**: Phase 0 is about finding 5-10 people who share your values and see the same problem. Quality of dialogue > quantity of responses.
|
||||
357
docs/outreach/PHASE-0-VALIDATION-LETTERS.md
Normal file
357
docs/outreach/PHASE-0-VALIDATION-LETTERS.md
Normal file
|
|
@ -0,0 +1,357 @@
|
|||
# Phase 0: Two-Stage Validation Letters
|
||||
|
||||
**Purpose**: Validate problem resonance (Stage 1) before asking for detailed feedback (Stage 2)
|
||||
**Approach**: Chunked time commitments (5 min → 10 min → 15 min)
|
||||
**Tone**: Professional, direct, no corporate BS
|
||||
**Date**: 30 October 2025
|
||||
|
||||
---
|
||||
|
||||
## Stage 1: Initial Exploratory Letter (5 Minutes Maximum)
|
||||
|
||||
**Purpose**: Quick reality check - does the problem resonate?
|
||||
**Time Ask**: 5 minutes
|
||||
**Response**: Simple yes/no/maybe + optional comment
|
||||
**Decision**: Only send Stage 2 to those who respond positively
|
||||
|
||||
---
|
||||
|
||||
### Template: Initial Exploratory Letter
|
||||
|
||||
**Subject**: Quick question - AI governance gap you're seeing?
|
||||
|
||||
Hi [Name],
|
||||
|
||||
Hope you're well. Quick question on something I've been working on - would value your perspective.
|
||||
|
||||
**Context**: I'm exploring what I'm calling the "governance mechanism gap" in AI deployment. Organizations deploying AI agents making thousands of decisions daily, but governance is mostly policies hoping the AI "behaves correctly." No architectural mechanisms to enforce boundaries before failures occur.
|
||||
|
||||
**Specific symptoms I'm seeing**:
|
||||
- AI overrides explicit human instructions when pattern recognition triggers
|
||||
- No way to surface value conflicts (privacy vs. utility) before AI chooses
|
||||
- Teams lose judgment capacity - "AI decides, we rubber-stamp"
|
||||
- No audit trails showing governance actually prevented failures
|
||||
|
||||
**Quick question** (5 minutes):
|
||||
|
||||
Are you seeing versions of this problem in [your organization / your field / projects you've worked on]?
|
||||
|
||||
**Quick response format**:
|
||||
- **YES** - Seeing this, want to know more
|
||||
- **MAYBE** - Seeing parts of this, not sure about others
|
||||
- **NO** - Not really seeing this / different challenges
|
||||
|
||||
Optional: One sentence on what you're seeing or not seeing.
|
||||
|
||||
That's it. No commitment, just a reality check from someone who's dealt with [governance at scale / AI in production / regulatory compliance / infrastructure projects / etc.].
|
||||
|
||||
If this doesn't resonate, no problem at all. If it does, I'd be interested in a follow-up conversation about specific angles.
|
||||
|
||||
Best,
|
||||
[Your name]
|
||||
|
||||
P.S. If curious about the approach: https://agenticgovernance.digital - but no need to read before responding, just your gut reaction to whether the problem description matches reality.
|
||||
|
||||
---
|
||||
|
||||
### Personalization Guide (Stage 1)
|
||||
|
||||
**For AI Forum NZ Member**:
|
||||
- Context addition: "...particularly relevant for Aotearoa given Te Tiriti governance principles around plural values"
|
||||
- Specific symptom: "Governance models imported from big tech that don't fit NZ context"
|
||||
- Their field: "AI governance discussions in NZ"
|
||||
|
||||
**For World Bank Legal (Retired)**:
|
||||
- Context addition: "...reminds me of governance theatre problems in international development"
|
||||
- Specific symptom: "Looks good on paper, doesn't enforce in practice"
|
||||
- Their field: "governance frameworks across different jurisdictions"
|
||||
|
||||
**For Tech Developer (Australia)**:
|
||||
- Context addition: "...technical problem, not just policy problem"
|
||||
- Specific symptom: "Context pressure causes LLMs to ignore instructions, more prompting doesn't help"
|
||||
- Their field: "production AI systems"
|
||||
|
||||
**For Video Content Creator**:
|
||||
- Context addition: "...affects small businesses using AI, not just enterprises"
|
||||
- Specific symptom: "AI makes decisions about client content without understanding brand/confidentiality requirements"
|
||||
- Their field: "AI-powered content creation"
|
||||
|
||||
**For Chief AI Governance Officer**:
|
||||
- Context addition: "...gap between what auditors ask for and what current approaches provide"
|
||||
- Specific symptom: "Asked 'how do you prove AI followed policies?' - we can't show architectural enforcement"
|
||||
- Their field: "enterprise AI governance"
|
||||
|
||||
**For Infrastructure Consultant (Retired)**:
|
||||
- Context addition: "...similar to implementation gaps in infrastructure governance"
|
||||
- Specific symptom: "Organizations deploy first, think about governance second"
|
||||
- Their field: "large-scale projects globally"
|
||||
|
||||
**For Academic Researcher**:
|
||||
- Context addition: "...research gap between AI alignment theory and deployment reality"
|
||||
- Specific symptom: "Training approaches assume AI 'learns' governance, no architectural enforcement"
|
||||
- Their field: "AI ethics/safety research"
|
||||
|
||||
**For Healthcare CIO**:
|
||||
- Context addition: "...particularly concerning in high-stakes contexts like healthcare"
|
||||
- Specific symptom: "AI makes decisions about patient data with no mechanism to enforce privacy rules before execution"
|
||||
- Their field: "healthcare AI deployment"
|
||||
|
||||
---
|
||||
|
||||
## Stage 2: Detailed Feedback Letter (Only if Stage 1 = YES/MAYBE)
|
||||
|
||||
**Purpose**: Get specific feedback on article angles
|
||||
**Time Ask**: Chunked into 5/10/15 minute options
|
||||
**Response**: Structured but flexible
|
||||
**Decision**: Determines which articles to write
|
||||
|
||||
---
|
||||
|
||||
### Template: Follow-Up Validation Letter
|
||||
|
||||
**Subject**: Re: AI governance - article angles (5/10/15 min options)
|
||||
|
||||
Hi [Name],
|
||||
|
||||
Thanks for confirming this resonates - helpful to know I'm not seeing patterns that don't exist.
|
||||
|
||||
I'm preparing to publish several articles exploring different angles of this governance gap. Before investing time writing them, I want to validate which angles would actually be valuable for people in [your field].
|
||||
|
||||
**Time options** (pick what works):
|
||||
|
||||
**5 minutes**: Quick scan of 5 article concepts, tell me which 1-2 would be most relevant for [your field]
|
||||
|
||||
**10 minutes**: Above + brief comment on what's missing from the angle you picked
|
||||
|
||||
**15 minutes**: Above + structured feedback on problem/solution framing
|
||||
|
||||
No pressure to do 15 - the 5-minute version is genuinely useful.
|
||||
|
||||
[ARTICLE CONCEPTS BELOW]
|
||||
|
||||
---
|
||||
|
||||
## 5 Article Concepts (Brief Descriptions)
|
||||
|
||||
### Version A: Organizational Hollowing & Judgment Atrophy
|
||||
|
||||
**Target**: Harvard Business Review, MIT Sloan
|
||||
**Angle**: Culture-conscious leaders worried about losing organizational judgment capacity
|
||||
|
||||
**Core argument**: When AI makes thousands of decisions daily using pattern recognition (not contextual judgment), teams lose capacity for nuanced decisions. Tacit knowledge stops transferring. Organizations become brittle. Current governance = policies hoping AI behaves. One architectural approach: mechanisms that preserve human judgment on value-sensitive decisions. Early evidence promising, broader validation ongoing.
|
||||
|
||||
**For whom**: Leaders who built teams on "je ne sais quoi" judgment, refuse to trade resilience for efficiency
|
||||
|
||||
---
|
||||
|
||||
### Version B: Architectural Compliance (GDPR Focus)
|
||||
|
||||
**Target**: Financial Times, Wall Street Journal
|
||||
**Angle**: GDPR officers needing audit-grade evidence of prevention
|
||||
|
||||
**Core argument**: GDPR fines €20M. Auditor asks "how did you prevent AI from exposing PII?" Answer: "We told it not to" isn't compliance evidence. Article 25 requires "data protection by design" - architectural safeguards. One approach: services that block PII exposure before execution, generate audit trails showing prevention occurred. Think this satisfies Article 25, regulatory validation ongoing.
|
||||
|
||||
**For whom**: Compliance professionals who need evidence not policies, risk managers, legal departments
|
||||
|
||||
---
|
||||
|
||||
### Version C: Why Behavioral Training Fails at Scale
|
||||
|
||||
**Target**: IEEE Spectrum, ACM Queue
|
||||
**Angle**: Engineers who've seen governance mechanisms fail in production
|
||||
|
||||
**Core argument**: You trained AI on 10,000 examples. In production it overrides human instructions when patterns conflict. More training increases override rate. "More training prolongs the pain." Training is probabilistic (shapes tendencies), governance requires deterministic (prevents failures). Technical deep-dive: 27027 Incident, context pressure failures, architectural constraints vs. behavioral approaches. Works in controlled deployment, validating at scale.
|
||||
|
||||
**For whom**: Production engineers, technical leaders, people who understand "hope-based governance" vs architectural
|
||||
|
||||
---
|
||||
|
||||
### Version D: Plural Moral Values Governance (Aotearoa Angle)
|
||||
|
||||
**Target**: The Daily Blog NZ, regional NZ/Pacific outlets
|
||||
**Angle**: Learning from Te Tiriti governance model
|
||||
|
||||
**Core argument**: Aotearoa has something to teach about governing systems where plural value frameworks coexist: Te Tiriti o Waitangi. Not value hierarchy, but mechanisms for plural values to navigate conflicts. AI faces same challenge: efficiency vs. equity, privacy vs. utility - incommensurable values. One architectural approach learns from this model. Small nations can lead governance innovation vs. importing extractive big tech approaches. Testing whether this works.
|
||||
|
||||
**For whom**: Culture-conscious leaders in NZ/Pacific, AI Forum NZ members, those concerned with plural values
|
||||
|
||||
---
|
||||
|
||||
### Version E: Comprehensive Governance Gap
|
||||
|
||||
**Target**: Substack (weekly series), Medium, LinkedIn
|
||||
**Angle**: Mixed technical + organizational + compliance audience
|
||||
|
||||
**Core argument**: Best decisions come from contextual judgment. AI makes thousands of decisions via pattern recognition - amoral intelligence making value-sensitive calls. Governance gap: no mechanisms to detect value decisions, surface conflicts, enforce human judgment, maintain audit trails. One architectural approach: six services. What's at stake: organizational hollowing. Early evidence from controlled deployment, broader validation ongoing. Honest uncertainty throughout.
|
||||
|
||||
**For whom**: Researchers, implementers, leaders - anyone interested in governance mechanisms
|
||||
|
||||
---
|
||||
|
||||
## Response Format Options
|
||||
|
||||
### OPTION 1: 5 Minutes (Quick Priority)
|
||||
|
||||
**Which 1-2 article concepts would be most valuable for people in [your field]?**
|
||||
|
||||
[ ] Version A: Organizational Hollowing (HBR/MIT Sloan)
|
||||
[ ] Version B: GDPR/Compliance (FT/WSJ)
|
||||
[ ] Version C: Technical Depth (IEEE/ACM)
|
||||
[ ] Version D: Aotearoa Governance (NZ/Pacific)
|
||||
[ ] Version E: Comprehensive (Substack/Medium)
|
||||
|
||||
**Optional**: One sentence on why.
|
||||
|
||||
**That's it - thanks!**
|
||||
|
||||
---
|
||||
|
||||
### OPTION 2: 10 Minutes (Priority + Gap)
|
||||
|
||||
Same as Option 1, plus:
|
||||
|
||||
**What's the biggest thing missing from the angle you picked?**
|
||||
(One paragraph)
|
||||
|
||||
---
|
||||
|
||||
### OPTION 3: 15 Minutes (Structured Feedback)
|
||||
|
||||
Same as Option 2, plus:
|
||||
|
||||
**Quick ratings** (1-5, 5=strongly resonates):
|
||||
|
||||
**Problem Framing**:
|
||||
- "Governance mechanism gap" describes real problem: [ ]
|
||||
- "Amoral AI" (no moral framework, just patterns): [ ]
|
||||
- "Judgment atrophy" (teams lose decision capacity): [ ]
|
||||
- "Hope-based governance" (policies without enforcement): [ ]
|
||||
|
||||
**Solution Framing**:
|
||||
- "Architectural constraints vs. behavioral training": [ ]
|
||||
- "Plural moral values" (organizations navigate own conflicts): [ ]
|
||||
- "Honest uncertainty" (we think it works, finding out): [ ]
|
||||
|
||||
**One concern or red flag**: (One paragraph)
|
||||
|
||||
---
|
||||
|
||||
## Closing
|
||||
|
||||
Whichever option works for you is genuinely helpful. Even the 5-minute "which article" response tells me a lot.
|
||||
|
||||
Happy to discuss over coffee/call if you prefer that to writing responses - just let me know.
|
||||
|
||||
Thanks for the perspective,
|
||||
[Your name]
|
||||
|
||||
---
|
||||
|
||||
## Post-Stage-2: Handling Responses
|
||||
|
||||
### If They Choose 5-Minute Option:
|
||||
- Thank them
|
||||
- Note which article they prioritized
|
||||
- No follow-up unless they volunteer to continue dialogue
|
||||
|
||||
### If They Choose 10-Minute Option:
|
||||
- Thank them
|
||||
- Incorporate "what's missing" into article
|
||||
- Offer to send draft when written (optional)
|
||||
|
||||
### If They Choose 15-Minute Option:
|
||||
- Thank them
|
||||
- Analyze ratings for validation
|
||||
- Incorporate feedback into article
|
||||
- Definitely offer to send draft
|
||||
- Ask if they'd be willing to continue dialogue
|
||||
|
||||
### If They Want to Continue:
|
||||
- "Would you be interested in occasional updates on how this develops? (What works, what fails, what we're still finding out)"
|
||||
- This identifies deeply aligned individuals for ongoing relationship
|
||||
|
||||
---
|
||||
|
||||
## Success Metrics by Stage
|
||||
|
||||
### Stage 1 Success:
|
||||
- **Minimum**: 3-5 respond YES/MAYBE
|
||||
- **Strong**: 5-8 respond YES/MAYBE
|
||||
- **Pivot**: Majority respond NO or don't respond
|
||||
|
||||
### Stage 2 Success:
|
||||
- **Minimum**: 3 choose Option 1 (5 min)
|
||||
- **Strong**: 5+ respond, 2+ choose Option 2/3 (10/15 min)
|
||||
- **Excellent**: Clear pattern in which articles prioritized + substantive feedback
|
||||
|
||||
### Overall Success (Validation Complete):
|
||||
- Know which 2-3 articles to write first
|
||||
- Incorporated "what's missing" feedback
|
||||
- Identified 2-3 people for ongoing dialogue
|
||||
- Average ratings >3.5 if anyone did Option 3
|
||||
|
||||
### Pivot Triggers:
|
||||
- Stage 1: <3 YES responses → Problem framing doesn't resonate
|
||||
- Stage 2: No clear pattern in article priorities → All angles equally weak/strong
|
||||
- Stage 2: Ratings <2.5 → Major reframing needed
|
||||
- Stage 2: Multiple "red flags" on same issue → Address before writing
|
||||
|
||||
---
|
||||
|
||||
## Profile → Recommended Articles Guide
|
||||
|
||||
| Profile | Likely Priority | Secondary | Rationale |
|
||||
|---------|----------------|-----------|-----------|
|
||||
| AI Forum NZ | Version D | A, E | NZ context, plural values, Te Tiriti connection |
|
||||
| World Bank Legal | Version B | A | Governance enforcement, regulatory credibility |
|
||||
| Tech Developer | Version C | E | Technical depth, production failures |
|
||||
| Video Creator | Version B | A | Client data protection, small business scale |
|
||||
| Chief AI Officer | Version B | A | Compliance evidence, enterprise scale |
|
||||
| Infrastructure Consultant | Version A | D | Cross-cultural governance, implementation reality |
|
||||
| Academic Researcher | Version C | E | Technical rigor, theoretical grounding |
|
||||
| Healthcare CIO | Version B | A | Safety-critical, regulatory compliance |
|
||||
|
||||
---
|
||||
|
||||
## Timeline
|
||||
|
||||
**Week 1**: Send Stage 1 letters to 8 contacts
|
||||
**Week 1-2**: Collect Stage 1 responses (5 min ask)
|
||||
**Week 2**: Send Stage 2 letters to YES/MAYBE respondents
|
||||
**Week 2-3**: Collect Stage 2 responses (5/10/15 min options)
|
||||
**Week 3**: Analyze feedback, prioritize articles
|
||||
**Week 4**: Write top 2 articles based on validation
|
||||
|
||||
---
|
||||
|
||||
## Tone Compliance Checklist
|
||||
|
||||
**Before sending, verify**:
|
||||
- [ ] No American corporate jargon ("leverage", "synergy", "value proposition")
|
||||
- [ ] No over-the-top enthusiasm ("exciting opportunity!", "revolutionary!")
|
||||
- [ ] Direct and professional (these are personal contacts)
|
||||
- [ ] Respects their time (chunked options, no guilt)
|
||||
- [ ] Honest about what you're asking and why
|
||||
- [ ] Easy to say no / easy to do minimal version
|
||||
- [ ] No pressure to adopt/buy/join anything
|
||||
|
||||
**Good phrases**:
|
||||
- ✅ "Would value your perspective"
|
||||
- ✅ "Quick reality check"
|
||||
- ✅ "Whichever option works for you"
|
||||
- ✅ "No problem at all if this doesn't resonate"
|
||||
- ✅ "We think it works, but we're finding out"
|
||||
|
||||
**Avoid phrases**:
|
||||
- ❌ "Exciting opportunity to be involved!"
|
||||
- ❌ "Revolutionary approach to AI governance"
|
||||
- ❌ "We'd love to have you on this journey"
|
||||
- ❌ "Game-changing solution"
|
||||
- ❌ "This will transform..."
|
||||
|
||||
---
|
||||
|
||||
**Status**: Ready for personalization and deployment
|
||||
**Documents**: 2 letters per contact (Stage 1 → Stage 2 if interested)
|
||||
**Total Time Ask**: 5 min (Stage 1) + optional 5/10/15 min (Stage 2)
|
||||
**Cultural DNA Compliance**: inst_088 (awakening, not recruiting), inst_086 (honest uncertainty)
|
||||
234
docs/outreach/PHASE-0-VALIDATION-TRACKER.md
Normal file
234
docs/outreach/PHASE-0-VALIDATION-TRACKER.md
Normal file
|
|
@ -0,0 +1,234 @@
|
|||
# Phase 0 Validation Tracker
|
||||
|
||||
**Status**: Active
|
||||
**Launch Date**: 29 October 2025
|
||||
**Goal**: Validate messaging with 5-10 aligned individuals before broader outreach
|
||||
**Success Metric**: Thoughtful feedback, not scale
|
||||
|
||||
---
|
||||
|
||||
## 📊 Publication Details
|
||||
|
||||
**Substack Article**: [Paste URL here]
|
||||
- **Title**: The Governance Mechanism Gap: What's Missing in AI Deployment
|
||||
- **Published**: 29 October 2025
|
||||
- **Tags**: AI Safety, AI Governance, Artificial Intelligence, Organizational Culture, AI Ethics
|
||||
- **Format**: Free subscription (validation phase)
|
||||
|
||||
**Website**: https://agenticgovernance.digital
|
||||
- **Analytics**: https://analytics.agenticgovernance.digital/websites/e09dad07-361b-453b-9e2c-2132c657d203
|
||||
|
||||
---
|
||||
|
||||
## 🎯 Validation Contacts (Target: 5-10)
|
||||
|
||||
### Contact 1: [Name]
|
||||
- **Profile**: [Researcher/Implementer/Leader]
|
||||
- **Context**: [Why they're aligned]
|
||||
- **Outreach Date**:
|
||||
- **Method**: [Email/LinkedIn/Direct]
|
||||
- **Status**: [ ] Not contacted / [ ] Contacted / [ ] Responded / [ ] Feedback received
|
||||
- **Response Summary**:
|
||||
- **Key Insights**:
|
||||
|
||||
### Contact 2: [Name]
|
||||
- **Profile**: [Researcher/Implementer/Leader]
|
||||
- **Context**: [Why they're aligned]
|
||||
- **Outreach Date**:
|
||||
- **Method**: [Email/LinkedIn/Direct]
|
||||
- **Status**: [ ] Not contacted / [ ] Contacted / [ ] Responded / [ ] Feedback received
|
||||
- **Response Summary**:
|
||||
- **Key Insights**:
|
||||
|
||||
### Contact 3: [Name]
|
||||
- **Profile**: [Researcher/Implementer/Leader]
|
||||
- **Context**: [Why they're aligned]
|
||||
- **Outreach Date**:
|
||||
- **Method**: [Email/LinkedIn/Direct]
|
||||
- **Status**: [ ] Not contacted / [ ] Contacted / [ ] Responded / [ ] Feedback received
|
||||
- **Response Summary**:
|
||||
- **Key Insights**:
|
||||
|
||||
### Contact 4: [Name]
|
||||
- **Profile**: [Researcher/Implementer/Leader]
|
||||
- **Context**: [Why they're aligned]
|
||||
- **Outreach Date**:
|
||||
- **Method**: [Email/LinkedIn/Direct]
|
||||
- **Status**: [ ] Not contacted / [ ] Contacted / [ ] Responded / [ ] Feedback received
|
||||
- **Response Summary**:
|
||||
- **Key Insights**:
|
||||
|
||||
### Contact 5: [Name]
|
||||
- **Profile**: [Researcher/Implementer/Leader]
|
||||
- **Context**: [Why they're aligned]
|
||||
- **Outreach Date**:
|
||||
- **Method**: [Email/LinkedIn/Direct]
|
||||
- **Status**: [ ] Not contacted / [ ] Contacted / [ ] Responded / [ ] Feedback received
|
||||
- **Response Summary**:
|
||||
- **Key Insights**:
|
||||
|
||||
### Contact 6: [Name] (Optional)
|
||||
- **Profile**: [Researcher/Implementer/Leader]
|
||||
- **Context**: [Why they're aligned]
|
||||
- **Outreach Date**:
|
||||
- **Method**: [Email/LinkedIn/Direct]
|
||||
- **Status**: [ ] Not contacted / [ ] Contacted / [ ] Responded / [ ] Feedback received
|
||||
- **Response Summary**:
|
||||
- **Key Insights**:
|
||||
|
||||
### Contact 7: [Name] (Optional)
|
||||
- **Profile**: [Researcher/Implementer/Leader]
|
||||
- **Context**: [Why they're aligned]
|
||||
- **Outreach Date**:
|
||||
- **Method**: [Email/LinkedIn/Direct]
|
||||
- **Status**: [ ] Not contacted / [ ] Contacted / [ ] Responded / [ ] Feedback received
|
||||
- **Response Summary**:
|
||||
- **Key Insights**:
|
||||
|
||||
### Contact 8: [Name] (Optional)
|
||||
- **Profile**: [Researcher/Implementer/Leader]
|
||||
- **Context**: [Why they're aligned]
|
||||
- **Outreach Date**:
|
||||
- **Method**: [Email/LinkedIn/Direct]
|
||||
- **Status**: [ ] Not contacted / [ ] Contacted / [ ] Responded / [ ] Feedback received
|
||||
- **Response Summary**:
|
||||
- **Key Insights**:
|
||||
|
||||
### Contact 9: [Name] (Optional)
|
||||
- **Profile**: [Researcher/Implementer/Leader]
|
||||
- **Context**: [Why they're aligned]
|
||||
- **Outreach Date**:
|
||||
- **Method**: [Email/LinkedIn/Direct]
|
||||
- **Status**: [ ] Not contacted / [ ] Contacted / [ ] Responded / [ ] Feedback received
|
||||
- **Response Summary**:
|
||||
- **Key Insights**:
|
||||
|
||||
### Contact 10: [Name] (Optional)
|
||||
- **Profile**: [Researcher/Implementer/Leader]
|
||||
- **Context**: [Why they're aligned]
|
||||
- **Outreach Date**:
|
||||
- **Method**: [Email/LinkedIn/Direct]
|
||||
- **Status**: [ ] Not contacted / [ ] Contacted / [ ] Responded / [ ] Feedback received
|
||||
- **Response Summary**:
|
||||
- **Key Insights**:
|
||||
|
||||
---
|
||||
|
||||
## 🔍 Validation Questions
|
||||
|
||||
Track which aspects of the article resonate or need refinement:
|
||||
|
||||
### Content Resonance
|
||||
- [ ] "Governance mechanism gap" framing clear?
|
||||
- [ ] "Amoral AI" concept understood?
|
||||
- [ ] Six services architecture compelling?
|
||||
- [ ] "Judgment atrophy" resonates with experience?
|
||||
- [ ] "Plural moral values" positioning clear?
|
||||
- [ ] Honest uncertainty tone appropriate?
|
||||
|
||||
### Audience Fit
|
||||
- [ ] Researchers: Sees research validity?
|
||||
- [ ] Implementers: Recognizes technical approach?
|
||||
- [ ] Leaders: Connects to organizational challenges?
|
||||
|
||||
### Messaging Issues
|
||||
- **What confused people?**:
|
||||
- **What technical details need clarification?**:
|
||||
- **What sections felt too abstract?**:
|
||||
- **What examples resonated most?**:
|
||||
|
||||
---
|
||||
|
||||
## 📈 Analytics Tracking
|
||||
|
||||
### Week 1 (29 Oct - 5 Nov)
|
||||
- **Substack Opens**:
|
||||
- **Click-through to website**:
|
||||
- **Website sessions**:
|
||||
- **Avg. time on site**:
|
||||
- **Pages visited**:
|
||||
|
||||
### Week 2 (6 Nov - 12 Nov)
|
||||
- **Substack Opens**:
|
||||
- **Click-through to website**:
|
||||
- **Website sessions**:
|
||||
- **Avg. time on site**:
|
||||
- **Pages visited**:
|
||||
|
||||
### Week 3 (13 Nov - 19 Nov)
|
||||
- **Substack Opens**:
|
||||
- **Click-through to website**:
|
||||
- **Website sessions**:
|
||||
- **Avg. time on site**:
|
||||
- **Pages visited**:
|
||||
|
||||
### Week 4 (20 Nov - 26 Nov)
|
||||
- **Substack Opens**:
|
||||
- **Click-through to website**:
|
||||
- **Website sessions**:
|
||||
- **Avg. time on site**:
|
||||
- **Pages visited**:
|
||||
|
||||
---
|
||||
|
||||
## 💡 Key Learnings
|
||||
|
||||
### What's Working
|
||||
1.
|
||||
2.
|
||||
3.
|
||||
|
||||
### What Needs Refinement
|
||||
1.
|
||||
2.
|
||||
3.
|
||||
|
||||
### Unexpected Insights
|
||||
1.
|
||||
2.
|
||||
3.
|
||||
|
||||
### Questions Raised
|
||||
1.
|
||||
2.
|
||||
3.
|
||||
|
||||
---
|
||||
|
||||
## 🎯 Phase 0 → Phase 1 Transition Criteria
|
||||
|
||||
**Ready to move to Phase 1 (Low-risk social exposure) when:**
|
||||
|
||||
- [ ] 5+ validation contacts have provided feedback
|
||||
- [ ] Core messaging validated (governance gap + architectural approach)
|
||||
- [ ] No major confusion about "amoral AI" or "plural moral values"
|
||||
- [ ] At least 2 contacts expressed "this matches my experience"
|
||||
- [ ] Technical accuracy confirmed by implementers/researchers
|
||||
- [ ] Iteration complete based on feedback
|
||||
|
||||
**Phase 1 Target Platforms:**
|
||||
- Hacker News
|
||||
- Reddit (r/MachineLearning, r/artificial)
|
||||
- LinkedIn (personal network)
|
||||
- ACM TechNews
|
||||
|
||||
---
|
||||
|
||||
## 📝 Notes & Observations
|
||||
|
||||
### Week 1
|
||||
-
|
||||
|
||||
### Week 2
|
||||
-
|
||||
|
||||
### Week 3
|
||||
-
|
||||
|
||||
### Week 4
|
||||
-
|
||||
|
||||
---
|
||||
|
||||
**Next Review**: [Date]
|
||||
**Status**: Phase 0 Active
|
||||
222
docs/outreach/PUBLICATION-RECORD.md
Normal file
222
docs/outreach/PUBLICATION-RECORD.md
Normal file
|
|
@ -0,0 +1,222 @@
|
|||
# Tractatus Publication Record
|
||||
|
||||
**Purpose**: Track all public content, URLs, and publication dates
|
||||
**Status**: Phase 0 - Personal Validation
|
||||
|
||||
---
|
||||
|
||||
## 📰 Phase 0 Publications
|
||||
|
||||
### Substack Article: "The Governance Mechanism Gap"
|
||||
|
||||
**Publication Date**: 29 October 2025
|
||||
**Status**: Published
|
||||
**Phase**: Phase 0 (Personal validation with 5-10 aligned individuals)
|
||||
|
||||
**Title**: The Governance Mechanism Gap: What's Missing in AI Deployment
|
||||
|
||||
**URL**: [PASTE SUBSTACK URL HERE]
|
||||
|
||||
**Subtitle**: Architectural constraints for plural moral values in AI governance
|
||||
|
||||
**Word Count**: ~1,820 words
|
||||
|
||||
**Tags**:
|
||||
- AI Safety
|
||||
- AI Governance
|
||||
- Artificial Intelligence
|
||||
- Organizational Culture
|
||||
- AI Ethics
|
||||
|
||||
**Subscription Model**: Free (Phase 0 validation)
|
||||
|
||||
**Images**:
|
||||
- Header image: None (published text-only)
|
||||
- Welcome page image: Attempted, not used
|
||||
|
||||
**Key Sections**:
|
||||
1. Opening (contextual judgment vs. amoral AI)
|
||||
2. The Amoral AI Reality
|
||||
3. Why Current Approaches Fail
|
||||
4. One Architectural Approach (Six Services)
|
||||
5. Unexpected Early Evidence
|
||||
6. Plural Moral Values in Practice
|
||||
7. What's At Stake (Organizational Hollowing)
|
||||
8. What This Is (And Isn't)
|
||||
9. What We're Testing
|
||||
10. Are You Seeing This?
|
||||
|
||||
**CTAs**:
|
||||
- Visit https://agenticgovernance.digital to explore framework
|
||||
- Subscribe for validation updates
|
||||
- Share with aligned individuals
|
||||
|
||||
**Analytics**:
|
||||
- Substack native analytics
|
||||
- Website referral tracking: https://analytics.agenticgovernance.digital/websites/e09dad07-361b-453b-9e2c-2132c657d203
|
||||
|
||||
---
|
||||
|
||||
## 🌐 Website
|
||||
|
||||
**Primary URL**: https://agenticgovernance.digital
|
||||
|
||||
**Launch Date**: October 2025 (Phase 1 development)
|
||||
|
||||
**Key Pages**:
|
||||
- Homepage (index.html)
|
||||
- For Researchers (researcher.html)
|
||||
- For Implementers (implementer.html)
|
||||
- For Leaders (leader.html)
|
||||
- About (about.html)
|
||||
|
||||
**Languages**:
|
||||
- English (en) - Complete
|
||||
- German (de) - Complete
|
||||
- French (fr) - Complete
|
||||
|
||||
**Share CTA**: Implemented on all pages (29 October 2025)
|
||||
|
||||
---
|
||||
|
||||
## 📱 Social Media
|
||||
|
||||
### Facebook
|
||||
**Launch Date**: 29 October 2025
|
||||
**Status**: Phase 0 posts published
|
||||
**Content**: Selected from FACEBOOK-POST-OPTIONS.md
|
||||
**Tracking**: Via Umami analytics
|
||||
|
||||
### LinkedIn
|
||||
**Status**: Pending Phase 0 validation completion
|
||||
**Plan**: Share Substack article with personal network
|
||||
|
||||
### Twitter/X
|
||||
**Status**: Not planned for Phase 0
|
||||
**Plan**: Consider for Phase 1
|
||||
|
||||
---
|
||||
|
||||
## 📄 Outreach Documents
|
||||
|
||||
### Primary Content
|
||||
- **VERSION-E-SUBSTACK-DRAFT.md** - Master article (with URLs added 29 Oct)
|
||||
- **Substack-Article-Governance-Gap.docx** - Ready-to-paste format
|
||||
- **FACEBOOK-POST-OPTIONS.md** - 11 social post variants
|
||||
|
||||
### Phase 0 Support Documents
|
||||
- **PHASE-0-VALIDATION-TRACKER.md** - Contact tracking & feedback
|
||||
- **PHASE-0-OUTREACH-TEMPLATES.md** - Email/LinkedIn message templates
|
||||
- **PHASE-0-FEEDBACK-COLLECTION.md** - Feedback methods & processes
|
||||
- **PUBLICATION-RECORD.md** - This document
|
||||
|
||||
### Brand Assets
|
||||
- **Tractatus-Architecture-Diagram.png** - Six services diagram
|
||||
- **Tractatus-Landing-Logo.svg** - Homepage logo
|
||||
- **Tractatus-Favicon-Large.png** - Large favicon
|
||||
- **Tractatus-Welcome-CleanWhite.png** - Welcome image (white bg)
|
||||
- **Tractatus-Welcome-LightBlue.png** - Welcome image (blue bg)
|
||||
- **Tractatus-Welcome-Gradient.png** - Welcome image (gradient bg)
|
||||
|
||||
---
|
||||
|
||||
## 📊 Analytics Setup
|
||||
|
||||
### Website (Umami)
|
||||
**URL**: https://analytics.agenticgovernance.digital/websites/e09dad07-361b-453b-9e2c-2132c657d203
|
||||
**Privacy**: GDPR compliant, no cookies, privacy-preserving
|
||||
**Tracking**:
|
||||
- Page views
|
||||
- Referral sources
|
||||
- Session duration
|
||||
- Geographic data (country-level only)
|
||||
|
||||
### Substack
|
||||
**Platform**: Native Substack analytics
|
||||
**Metrics**:
|
||||
- Opens
|
||||
- Clicks
|
||||
- Subscriber growth
|
||||
- Referral traffic
|
||||
|
||||
---
|
||||
|
||||
## 🎯 Phase Timeline
|
||||
|
||||
### Phase 0: Personal Validation (Current)
|
||||
**Start**: 29 October 2025
|
||||
**Duration**: 2-4 weeks
|
||||
**Goal**: 5-10 aligned individuals provide feedback
|
||||
**Metric**: Thoughtful dialogue, not scale
|
||||
|
||||
**Publications**:
|
||||
- ✅ Substack article published (29 Oct)
|
||||
- ✅ Facebook posts initiated (29 Oct)
|
||||
- ⏳ Email outreach to 5-10 validation contacts
|
||||
- ⏳ Feedback collection & analysis
|
||||
|
||||
### Phase 1: Low-Risk Social Exposure (Planned)
|
||||
**Start**: TBD (after Phase 0 validation)
|
||||
**Target Platforms**:
|
||||
- Hacker News
|
||||
- Reddit (r/MachineLearning, r/artificial)
|
||||
- LinkedIn (personal network)
|
||||
- ACM TechNews
|
||||
|
||||
**Goal**: Technical community validation
|
||||
**Metric**: Substantive dialogue
|
||||
|
||||
### Phase 2: Technical Validation (Planned)
|
||||
**Target Publications**:
|
||||
- IEEE Spectrum
|
||||
- ACM Queue
|
||||
- Ars Technica
|
||||
|
||||
**Goal**: Production engineer validation
|
||||
**Metric**: Substantive feedback
|
||||
|
||||
### Phase 3: Culture-Conscious Leader Outreach (Planned)
|
||||
**Target Publications**:
|
||||
- Harvard Business Review
|
||||
- MIT Sloan Management Review
|
||||
- Financial Times
|
||||
|
||||
**Goal**: 50-100 deeply aligned individuals
|
||||
**Metric**: Quality of engagement
|
||||
|
||||
---
|
||||
|
||||
## 📝 Version History
|
||||
|
||||
### VERSION-E-SUBSTACK-DRAFT.md
|
||||
- **Version E**: Published 29 October 2025
|
||||
- **Changes from Version D**: Added website URLs (2 locations)
|
||||
- **Status**: Current version
|
||||
|
||||
### Future Versions
|
||||
Document any post-Phase 0 refinements here.
|
||||
|
||||
---
|
||||
|
||||
## 🔗 Quick Reference Links
|
||||
|
||||
**Primary Article**: [PASTE SUBSTACK URL HERE]
|
||||
**Website**: https://agenticgovernance.digital
|
||||
**Analytics**: https://analytics.agenticgovernance.digital/websites/e09dad07-361b-453b-9e2c-2132c657d203
|
||||
**Validation Tracker**: docs/outreach/PHASE-0-VALIDATION-TRACKER.md
|
||||
|
||||
---
|
||||
|
||||
## ✅ Next Actions
|
||||
|
||||
1. **Add Substack URL** - Paste actual URL at top of this document
|
||||
2. **Update PHASE-0-VALIDATION-TRACKER.md** - Add Substack URL there too
|
||||
3. **Update PHASE-0-OUTREACH-TEMPLATES.md** - Replace [Substack URL] placeholders
|
||||
4. **Begin outreach** - Start contacting 5-10 validation contacts
|
||||
5. **Monitor analytics** - Check daily for first week, then weekly
|
||||
|
||||
---
|
||||
|
||||
**Last Updated**: 29 October 2025
|
||||
**Document Owner**: [Your name]
|
||||
**Status**: Active - Phase 0
|
||||
BIN
docs/outreach/Recent-QA-Summary-29Oct2025.docx
Normal file
BIN
docs/outreach/Recent-QA-Summary-29Oct2025.docx
Normal file
Binary file not shown.
BIN
docs/outreach/Substack-Article-Governance-Gap.docx
Normal file
BIN
docs/outreach/Substack-Article-Governance-Gap.docx
Normal file
Binary file not shown.
BIN
docs/outreach/Tractatus Framework Visual Design.png
Normal file
BIN
docs/outreach/Tractatus Framework Visual Design.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 1.6 MiB |
BIN
docs/outreach/Tractatus-Architecture-Diagram.png
Normal file
BIN
docs/outreach/Tractatus-Architecture-Diagram.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 886 KiB |
BIN
docs/outreach/Tractatus-FAQs-29-33.docx
Normal file
BIN
docs/outreach/Tractatus-FAQs-29-33.docx
Normal file
Binary file not shown.
134
docs/outreach/Tractatus-Landing-Logo.svg
Normal file
134
docs/outreach/Tractatus-Landing-Logo.svg
Normal file
|
|
@ -0,0 +1,134 @@
|
|||
<?xml version="1.0" encoding="UTF-8"?>
|
||||
<svg width="200" height="200" viewBox="0 0 200 200" xmlns="http://www.w3.org/2000/svg">
|
||||
<defs>
|
||||
<!-- Central core gradient (shared with Passport - cyan to blue) -->
|
||||
<radialGradient id="tractatusCore">
|
||||
<stop offset="0%" style="stop-color:#64ffda;stop-opacity:1" />
|
||||
<stop offset="70%" style="stop-color:#448aff;stop-opacity:1" />
|
||||
<stop offset="100%" style="stop-color:#0ea5e9;stop-opacity:1" />
|
||||
</radialGradient>
|
||||
|
||||
<!-- Service-specific gradients (6 governance services) -->
|
||||
<linearGradient id="serviceBoundary" x1="0%" y1="0%" x2="100%" y2="100%">
|
||||
<stop offset="0%" style="stop-color:#10b981;stop-opacity:1" />
|
||||
<stop offset="100%" style="stop-color:#059669;stop-opacity:1" />
|
||||
</linearGradient>
|
||||
|
||||
<linearGradient id="serviceInstruction" x1="0%" y1="0%" x2="100%" y2="100%">
|
||||
<stop offset="0%" style="stop-color:#6366f1;stop-opacity:1" />
|
||||
<stop offset="100%" style="stop-color:#4f46e5;stop-opacity:1" />
|
||||
</linearGradient>
|
||||
|
||||
<linearGradient id="serviceValidator" x1="0%" y1="0%" x2="100%" y2="100%">
|
||||
<stop offset="0%" style="stop-color:#8b5cf6;stop-opacity:1" />
|
||||
<stop offset="100%" style="stop-color:#7c3aed;stop-opacity:1" />
|
||||
</linearGradient>
|
||||
|
||||
<linearGradient id="servicePressure" x1="0%" y1="0%" x2="100%" y2="100%">
|
||||
<stop offset="0%" style="stop-color:#f59e0b;stop-opacity:1" />
|
||||
<stop offset="100%" style="stop-color:#d97706;stop-opacity:1" />
|
||||
</linearGradient>
|
||||
|
||||
<linearGradient id="serviceMetacognitive" x1="0%" y1="0%" x2="100%" y2="100%">
|
||||
<stop offset="0%" style="stop-color:#ec4899;stop-opacity:1" />
|
||||
<stop offset="100%" style="stop-color:#db2777;stop-opacity:1" />
|
||||
</linearGradient>
|
||||
|
||||
<linearGradient id="serviceDeliberation" x1="0%" y1="0%" x2="100%" y2="100%">
|
||||
<stop offset="0%" style="stop-color:#14b8a6;stop-opacity:1" />
|
||||
<stop offset="100%" style="stop-color:#0d9488;stop-opacity:1" />
|
||||
</linearGradient>
|
||||
|
||||
<linearGradient id="connectionGradient" x1="0%" y1="0%" x2="100%" y2="0%">
|
||||
<stop offset="0%" style="stop-color:#64ffda;stop-opacity:0.2" />
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||||
<stop offset="50%" style="stop-color:#64ffda;stop-opacity:0.5" />
|
||||
<stop offset="100%" style="stop-color:#64ffda;stop-opacity:0.2" />
|
||||
</linearGradient>
|
||||
|
||||
<filter id="dropShadow">
|
||||
<feDropShadow dx="0" dy="2" stdDeviation="3" flood-opacity="0.3"/>
|
||||
</filter>
|
||||
</defs>
|
||||
|
||||
<!-- Subtle background -->
|
||||
<circle cx="100" cy="100" r="95" fill="rgba(255,255,255,0.02)"/>
|
||||
|
||||
<!-- Orbital rings with subtle rotation animation -->
|
||||
<circle cx="100" cy="100" r="85" stroke="#64ffda" stroke-width="1" opacity="0.15" fill="none">
|
||||
<animate attributeName="opacity" values="0.15;0.25;0.15" dur="6s" repeatCount="indefinite"/>
|
||||
</circle>
|
||||
<circle cx="100" cy="100" r="70" stroke="#64ffda" stroke-width="1" opacity="0.25" fill="none">
|
||||
<animate attributeName="opacity" values="0.25;0.35;0.25" dur="5s" repeatCount="indefinite"/>
|
||||
</circle>
|
||||
<circle cx="100" cy="100" r="55" stroke="#64ffda" stroke-width="1" opacity="0.35" fill="none">
|
||||
<animate attributeName="opacity" values="0.35;0.45;0.35" dur="4s" repeatCount="indefinite"/>
|
||||
</circle>
|
||||
|
||||
<!-- Connection lines with pulsing animation -->
|
||||
<g opacity="0.4">
|
||||
<line x1="100" y1="100" x2="100" y2="35" stroke="url(#connectionGradient)" stroke-width="2">
|
||||
<animate attributeName="opacity" values="0.3;0.6;0.3" dur="4s" repeatCount="indefinite"/>
|
||||
</line>
|
||||
<line x1="100" y1="100" x2="156" y2="67.5" stroke="url(#connectionGradient)" stroke-width="2">
|
||||
<animate attributeName="opacity" values="0.3;0.6;0.3" dur="4s" begin="0.67s" repeatCount="indefinite"/>
|
||||
</line>
|
||||
<line x1="100" y1="100" x2="156" y2="132.5" stroke="url(#connectionGradient)" stroke-width="2">
|
||||
<animate attributeName="opacity" values="0.3;0.6;0.3" dur="4s" begin="1.33s" repeatCount="indefinite"/>
|
||||
</line>
|
||||
<line x1="100" y1="100" x2="100" y2="165" stroke="url(#connectionGradient)" stroke-width="2">
|
||||
<animate attributeName="opacity" values="0.3;0.6;0.3" dur="4s" begin="2s" repeatCount="indefinite"/>
|
||||
</line>
|
||||
<line x1="100" y1="100" x2="44" y2="132.5" stroke="url(#connectionGradient)" stroke-width="2">
|
||||
<animate attributeName="opacity" values="0.3;0.6;0.3" dur="4s" begin="2.67s" repeatCount="indefinite"/>
|
||||
</line>
|
||||
<line x1="100" y1="100" x2="44" y2="67.5" stroke="url(#connectionGradient)" stroke-width="2">
|
||||
<animate attributeName="opacity" values="0.3;0.6;0.3" dur="4s" begin="3.33s" repeatCount="indefinite"/>
|
||||
</line>
|
||||
</g>
|
||||
|
||||
<!-- Six governance service nodes with breathing animation (staggered) -->
|
||||
<!-- 1. BoundaryEnforcer (top) - Green -->
|
||||
<circle cx="100" cy="35" r="18" fill="url(#serviceBoundary)" filter="url(#dropShadow)" opacity="0.9">
|
||||
<animate attributeName="r" values="18;21;18" dur="4s" repeatCount="indefinite"/>
|
||||
</circle>
|
||||
|
||||
<!-- 2. InstructionPersistenceClassifier (top-right) - Indigo -->
|
||||
<circle cx="156" cy="67.5" r="18" fill="url(#serviceInstruction)" filter="url(#dropShadow)" opacity="0.9">
|
||||
<animate attributeName="r" values="18;21;18" dur="4s" begin="0.67s" repeatCount="indefinite"/>
|
||||
</circle>
|
||||
|
||||
<!-- 3. CrossReferenceValidator (bottom-right) - Purple -->
|
||||
<circle cx="156" cy="132.5" r="18" fill="url(#serviceValidator)" filter="url(#dropShadow)" opacity="0.9">
|
||||
<animate attributeName="r" values="18;21;18" dur="4s" begin="1.33s" repeatCount="indefinite"/>
|
||||
</circle>
|
||||
|
||||
<!-- 4. ContextPressureMonitor (bottom) - Amber -->
|
||||
<circle cx="100" cy="165" r="18" fill="url(#servicePressure)" filter="url(#dropShadow)" opacity="0.9">
|
||||
<animate attributeName="r" values="18;21;18" dur="4s" begin="2s" repeatCount="indefinite"/>
|
||||
</circle>
|
||||
|
||||
<!-- 5. MetacognitiveVerifier (bottom-left) - Rose -->
|
||||
<circle cx="44" cy="132.5" r="18" fill="url(#serviceMetacognitive)" filter="url(#dropShadow)" opacity="0.9">
|
||||
<animate attributeName="r" values="18;21;18" dur="4s" begin="2.67s" repeatCount="indefinite"/>
|
||||
</circle>
|
||||
|
||||
<!-- 6. PluralisticDeliberationOrchestrator (top-left) - Teal -->
|
||||
<circle cx="44" cy="67.5" r="18" fill="url(#serviceDeliberation)" filter="url(#dropShadow)" opacity="0.9">
|
||||
<animate attributeName="r" values="18;21;18" dur="4s" begin="3.33s" repeatCount="indefinite"/>
|
||||
</circle>
|
||||
|
||||
<!-- Central core with breathing animation -->
|
||||
<circle cx="100" cy="100" r="35" fill="url(#tractatusCore)" filter="url(#dropShadow)">
|
||||
<animate attributeName="r" values="35;38;35" dur="3s" repeatCount="indefinite"/>
|
||||
</circle>
|
||||
|
||||
<!-- Outer glow with pulsing -->
|
||||
<circle cx="100" cy="100" r="38" fill="none" stroke="rgba(100,255,218,0.2)" stroke-width="2">
|
||||
<animate attributeName="r" values="38;45;38" dur="3s" repeatCount="indefinite"/>
|
||||
<animate attributeName="opacity" values="0.2;0.4;0.2" dur="3s" repeatCount="indefinite"/>
|
||||
</circle>
|
||||
|
||||
<!-- Center symbol - "T" -->
|
||||
<circle cx="100" cy="100" r="28" fill="rgba(0,0,0,0.25)"/>
|
||||
<text x="100" y="110" text-anchor="middle" font-family="Arial, sans-serif" font-size="32" font-weight="bold" fill="white" opacity="0.95">T</text>
|
||||
</svg>
|
||||
|
After Width: | Height: | Size: 7 KiB |
|
|
@ -87,6 +87,8 @@ We think governance mechanisms for plural moral values are possible through arch
|
|||
|
||||
We think this works. We're finding out through controlled testing.
|
||||
|
||||
**Learn more**: Technical documentation and implementation details at https://agenticgovernance.digital
|
||||
|
||||
---
|
||||
|
||||
## Unexpected Early Evidence (Honest Uncertainty)
|
||||
|
|
@ -206,7 +208,7 @@ This is Phase 0—validation before public launch. We're sharing what we're test
|
|||
|
||||
**Next**: If this resonates, share it with someone who needs to see it—a researcher wrestling with AI alignment, an implementer deploying AI at scale, or a leader navigating AI governance decisions. Help us reach the people who need structural AI safety solutions.
|
||||
|
||||
And if you want updates on what we're learning (what works, what fails, what we're still finding out), subscribe for validation updates. If you're testing governance approaches in your organization, let's compare notes.
|
||||
And if you want updates on what we're learning (what works, what fails, what we're still finding out), visit **https://agenticgovernance.digital** to explore the framework or subscribe for validation updates. If you're testing governance approaches in your organization, let's compare notes.
|
||||
|
||||
**Cultural DNA**: Grounded in operational reality. Honest about uncertainty. One approach among possible others. Invitation to understand, not recruit. Architectural emphasis throughout.
|
||||
|
||||
|
|
|
|||
107
docs/outreach/tractatus-icon-new.svg
Normal file
107
docs/outreach/tractatus-icon-new.svg
Normal file
|
|
@ -0,0 +1,107 @@
|
|||
<?xml version="1.0" encoding="UTF-8"?>
|
||||
<svg width="200" height="200" viewBox="0 0 200 200" xmlns="http://www.w3.org/2000/svg">
|
||||
<defs>
|
||||
<!-- Central core gradient (shared with Passport - cyan to blue) -->
|
||||
<radialGradient id="tractatusCore">
|
||||
<stop offset="0%" style="stop-color:#64ffda;stop-opacity:1" />
|
||||
<stop offset="70%" style="stop-color:#448aff;stop-opacity:1" />
|
||||
<stop offset="100%" style="stop-color:#0ea5e9;stop-opacity:1" />
|
||||
</radialGradient>
|
||||
|
||||
<!-- Service-specific gradients (6 governance services) -->
|
||||
<!-- 1. BoundaryEnforcer - Green (safety, protection) -->
|
||||
<linearGradient id="serviceBoundary" x1="0%" y1="0%" x2="100%" y2="100%">
|
||||
<stop offset="0%" style="stop-color:#10b981;stop-opacity:1" />
|
||||
<stop offset="100%" style="stop-color:#059669;stop-opacity:1" />
|
||||
</linearGradient>
|
||||
|
||||
<!-- 2. InstructionPersistenceClassifier - Indigo (memory, persistence) -->
|
||||
<linearGradient id="serviceInstruction" x1="0%" y1="0%" x2="100%" y2="100%">
|
||||
<stop offset="0%" style="stop-color:#6366f1;stop-opacity:1" />
|
||||
<stop offset="100%" style="stop-color:#4f46e5;stop-opacity:1" />
|
||||
</linearGradient>
|
||||
|
||||
<!-- 3. CrossReferenceValidator - Purple (verification) -->
|
||||
<linearGradient id="serviceValidator" x1="0%" y1="0%" x2="100%" y2="100%">
|
||||
<stop offset="0%" style="stop-color:#8b5cf6;stop-opacity:1" />
|
||||
<stop offset="100%" style="stop-color:#7c3aed;stop-opacity:1" />
|
||||
</linearGradient>
|
||||
|
||||
<!-- 4. ContextPressureMonitor - Amber (alertness, monitoring) -->
|
||||
<linearGradient id="servicePressure" x1="0%" y1="0%" x2="100%" y2="100%">
|
||||
<stop offset="0%" style="stop-color:#f59e0b;stop-opacity:1" />
|
||||
<stop offset="100%" style="stop-color:#d97706;stop-opacity:1" />
|
||||
</linearGradient>
|
||||
|
||||
<!-- 5. MetacognitiveVerifier - Rose (reflection, thought) -->
|
||||
<linearGradient id="serviceMetacognitive" x1="0%" y1="0%" x2="100%" y2="100%">
|
||||
<stop offset="0%" style="stop-color:#ec4899;stop-opacity:1" />
|
||||
<stop offset="100%" style="stop-color:#db2777;stop-opacity:1" />
|
||||
</linearGradient>
|
||||
|
||||
<!-- 6. PluralisticDeliberationOrchestrator - Teal (balance, mediation) -->
|
||||
<linearGradient id="serviceDeliberation" x1="0%" y1="0%" x2="100%" y2="100%">
|
||||
<stop offset="0%" style="stop-color:#14b8a6;stop-opacity:1" />
|
||||
<stop offset="100%" style="stop-color:#0d9488;stop-opacity:1" />
|
||||
</linearGradient>
|
||||
|
||||
<!-- Connection lines gradient -->
|
||||
<linearGradient id="connectionGradient" x1="0%" y1="0%" x2="100%" y2="0%">
|
||||
<stop offset="0%" style="stop-color:#64ffda;stop-opacity:0.2" />
|
||||
<stop offset="50%" style="stop-color:#64ffda;stop-opacity:0.5" />
|
||||
<stop offset="100%" style="stop-color:#64ffda;stop-opacity:0.2" />
|
||||
</linearGradient>
|
||||
|
||||
<!-- Drop shadow for depth -->
|
||||
<filter id="dropShadow">
|
||||
<feDropShadow dx="0" dy="2" stdDeviation="3" flood-opacity="0.3"/>
|
||||
</filter>
|
||||
</defs>
|
||||
|
||||
<!-- Subtle background for contrast -->
|
||||
<circle cx="100" cy="100" r="95" fill="rgba(255,255,255,0.02)"/>
|
||||
|
||||
<!-- Orbital rings (3 layers - governance architecture) -->
|
||||
<circle cx="100" cy="100" r="85" stroke="#64ffda" stroke-width="1" opacity="0.15" fill="none"/>
|
||||
<circle cx="100" cy="100" r="70" stroke="#64ffda" stroke-width="1" opacity="0.25" fill="none"/>
|
||||
<circle cx="100" cy="100" r="55" stroke="#64ffda" stroke-width="1" opacity="0.35" fill="none"/>
|
||||
|
||||
<!-- Connection lines from center to each service node (hexagonal pattern) -->
|
||||
<g opacity="0.4">
|
||||
<line x1="100" y1="100" x2="100" y2="35" stroke="url(#connectionGradient)" stroke-width="2"/>
|
||||
<line x1="100" y1="100" x2="156" y2="67.5" stroke="url(#connectionGradient)" stroke-width="2"/>
|
||||
<line x1="100" y1="100" x2="156" y2="132.5" stroke="url(#connectionGradient)" stroke-width="2"/>
|
||||
<line x1="100" y1="100" x2="100" y2="165" stroke="url(#connectionGradient)" stroke-width="2"/>
|
||||
<line x1="100" y1="100" x2="44" y2="132.5" stroke="url(#connectionGradient)" stroke-width="2"/>
|
||||
<line x1="100" y1="100" x2="44" y2="67.5" stroke="url(#connectionGradient)" stroke-width="2"/>
|
||||
</g>
|
||||
|
||||
<!-- Six governance service nodes in hexagonal arrangement -->
|
||||
<!-- 1. BoundaryEnforcer (top) - Green -->
|
||||
<circle cx="100" cy="35" r="18" fill="url(#serviceBoundary)" filter="url(#dropShadow)" opacity="0.9"/>
|
||||
|
||||
<!-- 2. InstructionPersistenceClassifier (top-right) - Indigo -->
|
||||
<circle cx="156" cy="67.5" r="18" fill="url(#serviceInstruction)" filter="url(#dropShadow)" opacity="0.9"/>
|
||||
|
||||
<!-- 3. CrossReferenceValidator (bottom-right) - Purple -->
|
||||
<circle cx="156" cy="132.5" r="18" fill="url(#serviceValidator)" filter="url(#dropShadow)" opacity="0.9"/>
|
||||
|
||||
<!-- 4. ContextPressureMonitor (bottom) - Amber -->
|
||||
<circle cx="100" cy="165" r="18" fill="url(#servicePressure)" filter="url(#dropShadow)" opacity="0.9"/>
|
||||
|
||||
<!-- 5. MetacognitiveVerifier (bottom-left) - Rose -->
|
||||
<circle cx="44" cy="132.5" r="18" fill="url(#serviceMetacognitive)" filter="url(#dropShadow)" opacity="0.9"/>
|
||||
|
||||
<!-- 6. PluralisticDeliberationOrchestrator (top-left) - Teal -->
|
||||
<circle cx="44" cy="67.5" r="18" fill="url(#serviceDeliberation)" filter="url(#dropShadow)" opacity="0.9"/>
|
||||
|
||||
<!-- Central core (AI system being governed) -->
|
||||
<circle cx="100" cy="100" r="35" fill="url(#tractatusCore)" filter="url(#dropShadow)"/>
|
||||
|
||||
<!-- Subtle outer glow -->
|
||||
<circle cx="100" cy="100" r="38" fill="none" stroke="rgba(100,255,218,0.2)" stroke-width="2"/>
|
||||
|
||||
<!-- Center symbol - "T" for Tractatus -->
|
||||
<circle cx="100" cy="100" r="28" fill="rgba(0,0,0,0.25)"/>
|
||||
<text x="100" y="110" text-anchor="middle" font-family="Arial, sans-serif" font-size="32" font-weight="bold" fill="white" opacity="0.95">T</text>
|
||||
</svg>
|
||||
|
After Width: | Height: | Size: 5.6 KiB |
|
|
@ -110,7 +110,6 @@
|
|||
"paragraph_2": "Die menschlichen Gesellschaften haben jahrhundertelang gelernt, den moralischen Pluralismus durch verfassungsmäßige Gewaltenteilung, Föderalismus, Subsidiarität und deliberative Demokratie zu bewältigen. Diese Strukturen erkennen an, dass die legitime Autorität über Wertentscheidungen bei den betroffenen Gemeinschaften liegt und nicht bei weit entfernten Experten, die Anspruch auf universelle Weisheit erheben.",
|
||||
"paragraph_3": "Die Entwicklung der KI birgt die Gefahr, dass dieser Fortschritt rückgängig gemacht wird. Während sich die Fähigkeiten in einigen wenigen Labors konzentrieren, werden Wertentscheidungen, die Milliarden von Menschen betreffen, von kleinen Teams kodiert, die ihre besonderen moralischen Intuitionen in großem Maßstab anwenden. Nicht aus Böswilligkeit, sondern aus struktureller Notwendigkeit. Die Architektur aktueller KI-Systeme erfordert hierarchische Wertesysteme.",
|
||||
"paragraph_4": "Der Tractatus-Rahmen bietet eine Alternative: Trennen Sie, was universell sein muss (Sicherheitsgrenzen) von dem, was kontextabhängig sein sollte (Wertüberlegungen). Dadurch bleibt die menschliche Handlungsfähigkeit bei moralischen Entscheidungen erhalten, während die KI-Fähigkeit skaliert werden kann."
|
||||
}
|
||||
},
|
||||
"share_cta": {
|
||||
"heading": "Helfen Sie uns, die richtigen Leute zu erreichen.",
|
||||
|
|
|
|||
|
|
@ -385,7 +385,6 @@
|
|||
"pilots_link": "→ Fallstudie einreichen",
|
||||
"why_collab": "Warum zusammenarbeiten?",
|
||||
"why_collab_desc": "Tractatus adressiert echte Lücken in der KI-Sicherheit. Frühe Anwender gestalten die Entwicklung des Rahmens und erwerben Fachkenntnisse in der strukturellen KI-Governance - eine differenzierende Fähigkeit, wenn die regulatorischen Anforderungen reifen."
|
||||
}
|
||||
},
|
||||
"share_cta": {
|
||||
"heading": "Helfen Sie uns, die richtigen Leute zu erreichen.",
|
||||
|
|
|
|||
|
|
@ -165,7 +165,6 @@
|
|||
"research_foundations_desc": "Organisationstheoretische Grundlagen, empirische Beobachtungen, Validierungsstudien",
|
||||
"evaluation_note": "Bewertungsprozess: Organisationen, die Tractatus bewerten, folgen in der Regel folgenden Schritten: (1) Technische Überprüfung von Architekturmustern, (2) Piloteinsatz in der Entwicklungsumgebung, (3) Kontextspezifische Validierung mit Rechtsberatern, (4) Entscheidung, ob die Muster bestimmte regulatorische/Risikoanforderungen erfüllen.",
|
||||
"contact_note": "Projektinformationen und Kontaktangaben: Über Seite"
|
||||
}
|
||||
},
|
||||
"share_cta": {
|
||||
"heading": "Helfen Sie uns, die richtigen Leute zu erreichen.",
|
||||
|
|
|
|||
|
|
@ -362,7 +362,6 @@
|
|||
"success_note": "Hinweis: Wir erhalten Forschungsanfragen von Akademikern, Sicherheitsforschern und KI-Sicherheitsforschern. Die Antwortpriorität basiert auf methodischer Strenge und der Relevanz der Forschungsfrage, nicht auf der institutionellen Zugehörigkeit.",
|
||||
"close": "Schließen Sie"
|
||||
}
|
||||
}
|
||||
},
|
||||
"share_cta": {
|
||||
"heading": "Helfen Sie uns, die richtigen Leute zu erreichen.",
|
||||
|
|
|
|||
|
|
@ -110,7 +110,6 @@
|
|||
"paragraph_2": "Les sociétés humaines ont passé des siècles à apprendre à naviguer dans le pluralisme moral grâce à la séparation constitutionnelle des pouvoirs, au fédéralisme, à la subsidiarité et à la démocratie délibérative. Ces structures reconnaissent que l'autorité légitime sur les décisions relatives aux valeurs appartient aux communautés concernées, et non à des experts lointains prétendant à la sagesse universelle.",
|
||||
"paragraph_3": "Le développement de l'IA risque d'inverser ces progrès. Alors que les capacités se concentrent dans quelques laboratoires, les décisions de valeur affectant des milliards de personnes sont codées par de petites équipes qui appliquent leurs intuitions morales particulières à grande échelle. Ce n'est pas par malice, mais par nécessité structurelle. L'architecture des systèmes d'IA actuels exige des cadres de valeurs hiérarchiques.",
|
||||
"paragraph_4": "Le cadre du Tractatus propose une alternative : séparer ce qui doit être universel (limites de sécurité) de ce qui doit être contextuel (délibération sur les valeurs). Cela préserve la capacité de l'homme à prendre des décisions morales tout en permettant à l'IA d'évoluer."
|
||||
}
|
||||
},
|
||||
"share_cta": {
|
||||
"heading": "Aidez-nous à atteindre les bonnes personnes.",
|
||||
|
|
|
|||
|
|
@ -385,7 +385,6 @@
|
|||
"pilots_link": "→ Soumettre une étude de cas",
|
||||
"why_collab": "Pourquoi collaborer ?",
|
||||
"why_collab_desc": "Tractatus comble les lacunes réelles en matière de sécurité de l'IA. Les premiers utilisateurs façonnent l'évolution du cadre et acquièrent une expertise en matière de gouvernance structurelle de l'IA - une capacité différenciatrice à mesure que les exigences réglementaires évoluent."
|
||||
}
|
||||
},
|
||||
"share_cta": {
|
||||
"heading": "Aidez-nous à atteindre les bonnes personnes.",
|
||||
|
|
|
|||
|
|
@ -165,7 +165,6 @@
|
|||
"research_foundations_desc": "Base de la théorie organisationnelle, observations empiriques, études de validation",
|
||||
"evaluation_note": "Processus d'évaluation : Les organisations qui évaluent Tractatus suivent généralement les étapes suivantes : (1) Examen technique des modèles architecturaux, (2) Déploiement pilote dans un environnement de développement, (3) Validation spécifique au contexte avec un conseiller juridique, (4) Décision si les modèles répondent à des exigences spécifiques en matière de réglementation/risque.",
|
||||
"contact_note": "Informations sur le projet et coordonnées : Page d'accueil"
|
||||
}
|
||||
},
|
||||
"share_cta": {
|
||||
"heading": "Aidez-nous à atteindre les bonnes personnes.",
|
||||
|
|
|
|||
|
|
@ -362,7 +362,6 @@
|
|||
"success_note": "Remarque : Nous recevons des demandes de recherche de la part d'universitaires, de chercheurs en sécurité et d'enquêteurs sur la sécurité de l'IA. La priorité de réponse est basée sur la rigueur méthodologique et la pertinence de la question de recherche, et non sur l'affiliation institutionnelle.",
|
||||
"close": "Fermer"
|
||||
}
|
||||
}
|
||||
},
|
||||
"share_cta": {
|
||||
"heading": "Aidez-nous à atteindre les bonnes personnes.",
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue