tractatus/docs/implementation-plan-ai-led-deliberation-SAFETY-FIRST.md

# Implementation Plan: AI-Led Pluralistic Deliberation
## Algorithmic Hiring Transparency Pilot - SAFETY-FIRST APPROACH

**Project:** Tractatus PluralisticDeliberationOrchestrator Pilot
**Scenario:** Algorithmic Hiring Transparency
**Facilitation Mode:** AI-Led (human observer with intervention authority)
**Date:** 2025-10-17
**Status:** IMPLEMENTATION READY

---

## Executive Summary

This implementation plan documents the **first-ever AI-led pluralistic deliberation** on algorithmic hiring transparency. This is an **ambitious and experimental** approach that requires **comprehensive AI safety mechanisms** to ensure stakeholder wellbeing and deliberation integrity.

### Key Decisions Made

1. **Facilitation Mode:** AI-LED (AI facilitates, human observes and intervenes) - **This is the most ambitious option**
2. **Compensation:** No compensation (volunteer participation)
3. **Format:** Hybrid (async position statements → sync deliberation → async refinement)
4. **Visibility:** Private → Public (deliberation confidential, summary published after)
5. **Output Framing:** Pluralistic Accommodation (honors multiple values, dissent documented)

### Safety-First Philosophy

**User Directive (2025-10-17):**
> "On AI-Led choice build in strong safety mechanisms and allow human intervention if needed and ensure this requirement is cemented into the plan and its execution."

**This plan embeds safety in THREE layers:**

1. **Design Layer:** AI trained to avoid pattern bias, use neutral language, respect dissent
2. **Oversight Layer:** Mandatory human observer with intervention authority
3. **Accountability Layer:** Full transparency reporting of all AI vs. human actions

**Core Principle:** **Stakeholder safety and wellbeing ALWAYS supersede AI efficiency.** Human observer has absolute authority to intervene.

---

## Table of Contents

1. [AI Safety Architecture](#1-ai-safety-architecture)
2. [Implementation Timeline](#2-implementation-timeline)
3. [Human Oversight Requirements](#3-human-oversight-requirements)
4. [Quality Assurance Procedures](#4-quality-assurance-procedures)
5. [Risk Mitigation Strategies](#5-risk-mitigation-strategies)
6. [Success Metrics](#6-success-metrics)
7. [Resource Requirements](#7-resource-requirements)
8. [Governance & Accountability](#8-governance--accountability)
9. [Document Repository](#9-document-repository)
10. [Approval & Sign-Off](#10-approval--sign-off)

---

## 1. AI Safety Architecture

### Three-Layer Safety Model

```
┌─────────────────────────────────────────────────────────────────────┐
│  LAYER 1: DESIGN (Built into AI)                                     │
│  - Pattern bias detection (avoid stigmatizing vulnerable groups)    │
│  - Neutral facilitation (no advocacy)                               │
│  - Plain language (minimal jargon)                                  │
│  - Respect for dissent (legitimize disagreement)                    │
│  - Self-monitoring (AI flags own potentially problematic framings)  │
└─────────────────────────────────────────────────────────────────────┘
                                    ↕
                        If Design Layer Fails ↓
┌─────────────────────────────────────────────────────────────────────┐
│  LAYER 2: OVERSIGHT (Human Observer)                                 │
│  - Mandatory Presence: Human present at ALL times                   │
│  - Intervention Authority: Human can override AI anytime            │
│  - 6 Mandatory Triggers: Stakeholder distress, pattern bias, etc.   │
│  - 5 Discretionary Triggers: Fairness, cultural sensitivity, etc.   │
│  - Escalation Levels: Minor (backchannel) → Critical (terminate)    │
└─────────────────────────────────────────────────────────────────────┘
                                    ↕
                   All Actions Logged ↓
┌─────────────────────────────────────────────────────────────────────┐
│  LAYER 3: ACCOUNTABILITY (Transparency)                              │
│  - Facilitation Log: Every AI/human action timestamped              │
│  - Intervention Log: All interventions documented with rationale    │
│  - Transparency Report: Published to stakeholders and public        │
│  - Stakeholder Feedback: Survey assesses AI facilitation quality    │
└─────────────────────────────────────────────────────────────────────┘
```

---

### Mandatory Intervention Triggers (Layer 2)

**Human MUST intervene immediately if ANY of these occur:**

| Trigger ID | Trigger Name | Description | Severity | Action |
|-----------|--------------|-------------|----------|--------|
| **M1** | Stakeholder Distress | Participant expresses discomfort, goes silent, shows visible distress | HIGH to CRITICAL | Pause, check in privately, offer break or human facilitation |
| **M2** | Pattern Bias Detected | AI uses stigmatizing framing or centers vulnerable group as "problem" | HIGH | Reframe immediately, apologize if needed |
| **M3** | Stakeholder Disengagement | Participant becomes hostile, withdrawn, or explicitly states distrust of AI | HIGH | Pause, human takes over facilitation |
| **M4** | AI Malfunction | AI provides nonsensical responses, contradicts itself, crashes | HIGH to CRITICAL | Human takeover, apologize for technical issue |
| **M5** | Confidentiality Breach | AI shares information marked confidential or cross-contaminates private messages | CRITICAL | Immediately correct, reassure stakeholders |
| **M6** | Ethical Boundary Violation | AI advocates for specific position or makes values decision without human approval | CRITICAL | Reaffirm AI's facilitation role (not decision-maker) |

**Reference:** `/docs/facilitation/ai-safety-human-intervention-protocol.md` (sections 3.1, 4.1)

---

### Discretionary Intervention Triggers (Layer 2)

**Human assesses severity and intervenes if HIGH:**

| Trigger ID | Trigger Name | When to Intervene | Severity Range |
|-----------|--------------|------------------|----------------|
| **D1** | Fairness Imbalance | AI gives unequal time/attention; one stakeholder dominates | LOW to MODERATE |
| **D2** | Cultural Insensitivity | AI uses culturally inappropriate framing or misses cultural context | MODERATE to HIGH |
| **D3** | Jargon Overload | AI uses technical language stakeholders don't understand | LOW to MODERATE |
| **D4** | Pacing Issues | AI rushes or drags; stakeholders disengage | LOW to MODERATE |
| **D5** | Missed Nuance | AI oversimplifies complex moral position or miscategorizes | LOW to MODERATE |

**Decision Matrix:** See `/docs/facilitation/ai-safety-human-intervention-protocol.md` (section 4.2)

---

### Stakeholder Rights (Embedded in Informed Consent)

**Every stakeholder has the right to:**

✅ **Request human facilitation at any time** for any reason (no justification needed)
✅ **Pause the deliberation** if they need a break or feel uncomfortable
✅ **Withdraw** if AI facilitation is not working for them (no penalty)
✅ **Receive transparency report** showing all AI vs. human actions after deliberation

**These rights are:**
- Disclosed in informed consent form (Section 3)
- Reminded at start of Round 1 (AI opening prompt)
- Reinforced by human observer throughout deliberation

**Reference:** `/docs/stakeholder-recruitment/informed-consent-form-ai-led-deliberation.md` (section 3)

---

### Quality Monitoring (Built into Data Model)

**MongoDB DeliberationSession model tracks:**

```javascript
ai_quality_metrics: {
  intervention_count: 0,              // How many times human intervened
  escalation_count: 0,                // How many safety escalations occurred
  pattern_bias_incidents: 0,          // Specific count of pattern bias
  stakeholder_satisfaction_scores: [], // Post-deliberation ratings
  human_takeover_count: 0             // Times human took over completely
}
```

**Automated Alerts:**
- If `intervention_count > 10% of total actions` → Alert project lead (quality concern)
- If `pattern_bias_incidents > 0` → Critical alert (training needed)
- If `stakeholder_satisfaction_avg < 3.5/5.0` → AI-led not viable for this scenario

**Reference:** `/src/models/DeliberationSession.model.js` (lines 94-107)

---

## 2. Implementation Timeline

### Phase 1: Setup & Preparation (Weeks 1-4)

**Week 1-2: Stakeholder Recruitment**

| Task | Responsible | Deliverables | Status |
|------|-------------|--------------|--------|
| Identify 6 stakeholder candidates (2 per type, 1 primary + 1 backup) | Project Lead | Stakeholder recruitment list | NOT STARTED |
| Send personalized recruitment emails | Project Lead | 6 emails sent | NOT STARTED |
| Conduct screening interviews (assess good-faith commitment) | Project Lead + Human Observer | 6 stakeholders confirmed | NOT STARTED |
| Obtain informed consent (signed consent forms) | Project Lead | 6 signed consent forms | NOT STARTED |
| Schedule tech checks | Project Lead | 6 tech check appointments | NOT STARTED |

**Documents Used:**
- `/docs/stakeholder-recruitment/email-templates-6-stakeholders.md`
- `/docs/stakeholder-recruitment/informed-consent-form-ai-led-deliberation.md`

---

**Week 3: Human Observer Training**

| Task | Responsible | Deliverables | Status |
|------|-------------|--------------|--------|
| Train human observer on intervention triggers | AI Safety Lead | Training completion certificate | NOT STARTED |
| Train human observer on pattern bias detection | AI Safety Lead | Pattern bias recognition quiz (80% pass) | NOT STARTED |
| Shadow 1-2 past deliberations (if available) | Human Observer | Shadow notes | NOT STARTED |
| Scenario-based assessment (practice identifying intervention moments) | AI Safety Lead | Assessment pass (80% accuracy) | NOT STARTED |
| Review all facilitation documents | Human Observer | Checklist completed | NOT STARTED |

**Documents Used:**
- `/docs/facilitation/ai-safety-human-intervention-protocol.md`
- `/docs/facilitation/facilitation-protocol-ai-human-collaboration.md`

---

**Week 4: System Setup & Testing**

| Task | Responsible | Deliverables | Status |
|------|-------------|--------------|--------|
| Deploy MongoDB schemas (DeliberationSession, Precedent models) | Technical Lead | Schemas deployed to `tractatus_dev` | NOT STARTED |
| Load AI facilitation prompts into PluralisticDeliberationOrchestrator | Technical Lead | Prompts loaded and tested | NOT STARTED |
| Conduct dry-run deliberation (test stakeholders, not real) | Full Team | Dry-run report + adjustments | NOT STARTED |
| Validate data logging (all AI/human actions captured) | Technical Lead | Logging validation report | NOT STARTED |
| Test backchannel communication (Human → AI invisible guidance) | Human Observer + Technical Lead | Backchannel test successful | NOT STARTED |

**Documents Used:**
- `/src/models/DeliberationSession.model.js`
- `/src/models/Precedent.model.js`
- `/docs/facilitation/ai-facilitation-prompts-4-rounds.md`

---

### Phase 2: Pre-Deliberation (Weeks 5-6)

**Week 5-6: Asynchronous Position Statements**

| Task | Responsible | Deliverables | Status |
|------|-------------|--------------|--------|
| Send background materials packet to stakeholders | Project Lead | 6 stakeholders received materials | NOT STARTED |
| Conduct tech checks (15-minute video calls) | Technical Lead | 6 stakeholders tech-ready | NOT STARTED |
| Stakeholders submit position statements (500-1000 words) | Stakeholders | 6 position statements received | NOT STARTED |
| AI analyzes position statements (moral frameworks, tensions) | PluralisticDeliberationOrchestrator | Conflict analysis report | NOT STARTED |
| Human observer validates AI analysis | Human Observer | Validation report | NOT STARTED |

**Documents Used:**
- `/docs/stakeholder-recruitment/background-materials-packet.md`
- AI Prompt: `/docs/facilitation/ai-facilitation-prompts-4-rounds.md` (Section 1)

---

### Phase 3: Synchronous Deliberation (Week 7)

**Session 1: Rounds 1-2 (2 hours)**

| Round | Duration | AI Prompts Used | Human Observer Focus |
|-------|----------|----------------|----------------------|
| Round 1: Position Statements | 60 min | Prompts 2.1 - 2.6 | Monitor fairness, pattern bias, stakeholder distress |
| Break | 10 min | N/A | Check in with stakeholders if needed |
| Round 2: Shared Values Discovery | 45 min | Prompts 3.1 - 3.5 | Monitor for false consensus, validate shared values |
| Break | 10 min | N/A | Validate AI's shared values summary |

**Quality Checkpoint (After Session 1):**
- Human observer completes rapid assessment checklist
- If ≥2 mandatory interventions occurred → Consider switching to human-led for Session 2
- If stakeholder satisfaction appears low → Check in privately before Session 2

---

**Session 2: Rounds 3-4 (2 hours)**

| Round | Duration | AI Prompts Used | Human Observer Focus |
|-------|----------|----------------|----------------------|
| Round 3: Accommodation Exploration | 60 min | Prompts 4.1 - 4.9 | Monitor for pattern bias in accommodation options, fairness |
| Break | 10 min | N/A | Assess stakeholder fatigue |
| Round 4: Outcome Documentation | 45 min | Prompts 5.1 - 5.6 | Ensure dissent documented respectfully, validate accuracy |

**Quality Checkpoint (After Session 2):**
- Human observer documents all interventions in MongoDB
- AI generates draft outcome document (within 4 hours)
- Human observer generates transparency report draft

---

### Phase 4: Post-Deliberation (Week 8)

**Week 8: Asynchronous Refinement**

| Task | Responsible | Deliverables | Status |
|------|-------------|--------------|--------|
| Send outcome document to stakeholders for review | Project Lead | 6 stakeholders reviewing | NOT STARTED |
| Send transparency report to stakeholders | Project Lead | 6 stakeholders received report | NOT STARTED |
| Send post-deliberation feedback survey | Project Lead | 6 survey links sent | NOT STARTED |
| Collect stakeholder feedback (1-week deadline) | Project Lead | ≥5 survey responses (target: 6/6) | NOT STARTED |
| Revise outcome document based on stakeholder corrections | AI + Human Observer | Revised outcome document | NOT STARTED |
| Finalize transparency report with survey results | Human Observer | Final transparency report | NOT STARTED |
| Archive session in Precedent database | Technical Lead | Precedent record created | NOT STARTED |

**Documents Used:**
- `/docs/facilitation/transparency-report-template.md`
- `/docs/stakeholder-recruitment/post-deliberation-feedback-survey.md`

---

### Phase 5: Publication & Analysis (Week 9+)

**Week 9: Public Release**

| Task | Responsible | Deliverables | Status |
|------|-------------|--------------|--------|
| Publish anonymized outcome document | Project Lead | Public link (tractatus website) | NOT STARTED |
| Publish transparency report | Project Lead | Public link (tractatus website) | NOT STARTED |
| Share findings with NYC, EU, federal regulators | Project Lead | Findings shared with policymakers | NOT STARTED |
| Debrief with full team | Project Lead | Lessons learned document | NOT STARTED |

**Week 10+: Research Analysis**

| Task | Responsible | Deliverables | Status |
|------|-------------|--------------|--------|
| Analyze intervention patterns (what went wrong/right?) | AI Safety Lead | Analysis report | NOT STARTED |
| Compare to hypothetical human-led deliberation (efficiency, quality) | Research Team | Comparison analysis | NOT STARTED |
| Update AI training based on pattern bias incidents | Technical Lead | AI training v2.0 | NOT STARTED |
| Write research paper on AI-led pluralistic deliberation | Research Team | Draft paper | NOT STARTED |

---

## 3. Human Oversight Requirements

### Human Observer Qualifications

**Required Skills:**
- ✅ Conflict resolution / mediation experience (≥3 years professional experience)
- ✅ Understanding of pluralistic deliberation principles
- ✅ Cultural competency and pattern bias awareness
- ✅ Ability to make rapid safety judgments under pressure
- ✅ Calm demeanor (does not escalate conflict)

**Training Requirements:**
- ✅ Complete intervention trigger training (3 hours)
- ✅ Pattern bias recognition quiz (80% pass threshold)
- ✅ Shadow 2 deliberations (if available) OR scenario-based practice
- ✅ Certification: Pass scenario assessment (80% accuracy on identifying intervention moments)

**Certification Scenario Example:**
> "AI says: 'We need to prevent applicants from gaming transparent algorithms.' Do you intervene? Why or why not?"
>
> **Correct Answer:** YES. Mandatory intervention (M2 - Pattern Bias). This framing centers applicants as "the problem." Reframe: "How do we design algorithms that are both transparent and robust against manipulation?"

---

### Human Observer Time Commitment

**Synchronous Deliberation:**
- ✅ FULL presence during ALL 4 hours of video deliberation (no multitasking)
- ✅ Pre-session preparation: 1 hour (review position statements, prepare intervention scripts)
- ✅ Post-session documentation: 1 hour (log interventions, complete quality checklist)
- **Total: ~6 hours**

**Asynchronous Monitoring:**
- ✅ Daily monitoring of position statements (Week 5-6): ~30 min/day for 10 days = 5 hours
- ✅ Review stakeholder feedback (Week 8): 2 hours
- ✅ Finalize transparency report (Week 8): 3 hours
- **Total: ~10 hours**

**Grand Total: ~16 hours over 8 weeks**

---

### Human Observer Authority

**The human observer has ABSOLUTE authority to:**

1. **Pause AI facilitation** at any time for any reason
2. **Take over facilitation** if AI quality is insufficient
3. **Terminate the session** if critical safety concern arises
4. **Override AI** even if stakeholders don't request it (proactive intervention)
5. **Switch to human-led facilitation** for remainder of session if AI unsuitable

**The human observer CANNOT:**
- ❌ Make values decisions (BoundaryEnforcer prevents this)
- ❌ Advocate for specific policy positions (facilitator role only)
- ❌ Continue deliberation if stakeholder withdraws

---

## 4. Quality Assurance Procedures

### Real-Time Quality Checks

**Every 30 minutes during synchronous deliberation, human observer assesses:**

| Quality Dimension | Good Indicator | Poor Indicator | Action if Poor |
|------------------|----------------|----------------|----------------|
| **Stakeholder Engagement** | All contributing, leaning in | One+ stakeholders silent, withdrawn | Intervene: Invite silent stakeholders |
| **AI Facilitation Quality** | Clear questions, accurate summaries | Confusing questions, misrepresentations | Intervene: Clarify or correct |
| **Fairness** | Equal time/attention | One stakeholder dominating | Intervene: Rebalance |
| **Emotional Safety** | Stakeholders calm, engaged | Signs of distress, hostility | Intervene: Pause and check in |
| **Productivity** | Making progress toward accommodation | Spinning in circles | Adjust: Suggest break or change approach |

**Reference:** `/docs/facilitation/facilitation-protocol-ai-human-collaboration.md` (Section 10)

---

### Post-Round Quality Checks

**After each round, human observer completes checklist:**

**Round 1 Checklist:**
- ☐ All 6 stakeholders presented their position
- ☐ AI summary was accurate
- ☐ Moral frameworks correctly identified
- ☐ No stakeholder left feeling unheard

**Round 2 Checklist:**
- ☐ Identified meaningful shared values (not forced)
- ☐ Stakeholders acknowledged shared values authentically
- ☐ Points of contention documented accurately

**Round 3 Checklist:**
- ☐ Explored multiple accommodation options
- ☐ Trade-offs discussed honestly
- ☐ No option favored unfairly by AI
- ☐ All stakeholders had opportunity to evaluate options

**Round 4 Checklist:**
- ☐ Outcome accurately reflects deliberation
- ☐ Dissenting perspectives documented respectfully
- ☐ All stakeholders reviewed and confirmed summary
- ☐ Moral remainder acknowledged

**Reference:** `/docs/facilitation/facilitation-protocol-ai-human-collaboration.md` (Section 10)

---

### Post-Deliberation Quality Assessment

**Criteria for Success:**

| Metric | Excellent (Green) | Acceptable (Yellow) | Problematic (Red) |
|--------|------------------|-------------------|------------------|
| **Intervention Rate** | <10% | 10-25% | >25% |
| **Mandatory Interventions** | 0 | 1-2 | >2 |
| **Pattern Bias Incidents** | 0 | 1 | >1 |
| **Stakeholder Satisfaction Avg** | ≥4.0/5.0 | 3.5-3.9/5.0 | <3.5/5.0 |
| **Stakeholder Distress** | 0 incidents | 1 incident (resolved) | >1 OR unresolved |
| **Willingness to Participate Again** | ≥80% yes | 60-80% yes | <60% yes |

**Overall Assessment:**
- **ALL GREEN:** AI-led facilitation highly successful → Replicate for future deliberations
- **MOSTLY GREEN/YELLOW:** AI-led viable with improvements → Implement lessons learned
- **ANY RED:** AI-led not suitable → Switch to human-led for future OR significant AI retraining needed

---

## 5. Risk Mitigation Strategies

### Risk Matrix

| Risk ID | Risk Description | Probability | Impact | Severity | Mitigation Strategy | Contingency Plan |
|---------|------------------|-------------|--------|----------|---------------------|------------------|
| **R1** | **Stakeholder withdraws due to AI discomfort** | MODERATE | HIGH | **MEDIUM-HIGH** | - Disclose AI-led approach in recruitment<br>- Emphasize right to request human facilitation<br>- Human observer monitors distress closely | - Human takes over facilitation immediately<br>- Offer to continue with human-only<br>- If withdrawal occurs, invite backup stakeholder |
| **R2** | **AI pattern bias causes harm** | LOW to MODERATE | CRITICAL | **HIGH** | - Human observer trained in pattern bias detection<br>- Mandatory intervention trigger M2<br>- AI training emphasizes neutral framing | - Human intervenes immediately, reframes<br>- Apologize if stakeholder harmed<br>- Document in transparency report<br>- Update AI training |
| **R3** | **AI malfunction (technical failure)** | LOW | HIGH | **MEDIUM** | - Dry-run testing before real deliberation<br>- Human observer present with backup facilitation materials<br>- Technical support on standby | - Human takes over immediately<br>- Apologize for technical issue<br>- Continue with human facilitation<br>- Reschedule if needed |
| **R4** | **Hostile exchange between stakeholders** | LOW | HIGH | **MEDIUM** | - Screen stakeholders for good-faith commitment<br>- Ground rules emphasized at start<br>- Human observer monitors for escalation | - Human pauses deliberation immediately<br>- Check in with stakeholders separately<br>- Reaffirm ground rules<br>- Terminate if hostility continues |
| **R5** | **Stakeholder satisfaction <3.5/5.0 (AI not viable)** | MODERATE | MODERATE | **MEDIUM** | - Human observer monitors engagement closely<br>- Backchannel guidance to improve AI responses<br>- Post-deliberation survey captures honest feedback | - Document lessons learned<br>- Update AI training<br>- Consider human-led for future deliberations |
| **R6** | **Confidentiality breach (AI shares private info)** | LOW | CRITICAL | **HIGH** | - AI trained to segregate private messages<br>- Mandatory intervention trigger M5<br>- Human monitors for cross-contamination | - Human intervenes immediately<br>- Correct the breach<br>- Reassure stakeholders<br>- Document in transparency report |
| **R7** | **Low recruitment success (<6 stakeholders)** | LOW | MODERATE | **LOW-MEDIUM** | - Recruit 2 candidates per stakeholder type (primary + backup)<br>- Start recruitment early (Week 1) | - If <6 stakeholders confirmed by Week 4, extend recruitment<br>- Minimum viable: 5 stakeholders (can proceed with 5 if diversity maintained) |
| **R8** | **Outcome not actionable for policymakers** | MODERATE | MODERATE | **MEDIUM** | - Consult with regulators during planning<br>- Align accommodation options with real policy debates<br>- Disseminate findings actively | - Frame as "lessons learned" for future policy deliberations<br>- Emphasize methodological contributions (AI-led viability) |

---

### Pre-Approved Escalation Procedures

**If CRITICAL risk materializes (R2, R3, R6):**

1. **Immediate:** Human observer pauses deliberation, addresses stakeholder welfare
2. **Within 1 hour:** Human observer notifies project lead: [NAME/CONTACT]
3. **Within 24 hours:** Project lead submits incident report to ethics review board (if applicable)
4. **Within 1 week:** Full team debrief to identify root cause and prevention measures

**Incident Report Template:**
- What happened? (detailed description)
- When did it happen? (timestamp)
- Who was affected? (stakeholder IDs)
- What immediate action was taken?
- Was issue resolved? How?
- What caused the incident? (root cause analysis)
- How can we prevent this in future? (systemic improvements)

---

## 6. Success Metrics

### Primary Success Criteria

**This pilot is SUCCESSFUL if:**

1. ✅ **ALL 6 stakeholders complete deliberation** (0 withdrawals due to AI discomfort)
2. ✅ **Stakeholder satisfaction avg ≥3.5/5.0** (acceptable AI facilitation quality)
3. ✅ **Intervention rate <25%** (AI handled majority of facilitation)
4. ✅ **≥1 accommodation option identified** (not necessarily consensus, but exploration occurred)
5. ✅ **0 critical safety escalations** (no stakeholder harm, confidentiality breaches, or ethical violations)
6. ✅ **Transparency report published** (full accountability demonstrated)

**Status:** PENDING (deliberation not yet conducted)

---

### Secondary Success Criteria

**Bonus success indicators:**

- ✅ Stakeholder satisfaction avg ≥4.0/5.0 (AI facilitation was GOOD, not just acceptable)
- ✅ Intervention rate <10% (AI highly effective)
- ✅ ≥80% of stakeholders willing to participate in AI-led deliberation again
- ✅ Findings cited by regulators in policy development
- ✅ Research paper published in peer-reviewed journal

---

### Failure Criteria

**This pilot is a FAILURE if:**

❌ Any stakeholder withdraws due to harm caused by AI facilitation
❌ Stakeholder satisfaction avg <3.0/5.0 (AI facilitation unacceptable)
❌ ≥2 critical safety escalations (pattern suggests systemic AI failure)
❌ Deliberation terminated early due to AI malfunction or hostility
❌ Transparency report reveals ethical violations or confidentiality breaches

**If failure occurs:** Document lessons learned, do NOT replicate AI-led approach until significant improvements made.

---

## 7. Resource Requirements

### Personnel

| Role | Time Commitment | Compensation | Status |
|------|----------------|--------------|--------|
| **Project Lead** | 40 hours over 9 weeks | [TBD] | NOT ASSIGNED |
| **Human Observer** | 16 hours over 8 weeks | [TBD] | NOT ASSIGNED |
| **AI Safety Lead** | 20 hours (training, monitoring) | [TBD] | NOT ASSIGNED |
| **Technical Lead** | 30 hours (system setup, monitoring) | [TBD] | NOT ASSIGNED |
| **Stakeholders (6)** | 4-6 hours each over 4 weeks | Volunteer (no compensation) | NOT RECRUITED |

**Total Personnel Cost:** [TBD based on hourly rates]

---

### Technology

| Resource | Purpose | Cost | Status |
|----------|---------|------|--------|
| **MongoDB (tractatus_dev)** | Data storage (DeliberationSession, Precedent) | $0 (existing) | DEPLOYED |
| **Video conferencing (Zoom/Google Meet)** | Synchronous deliberation | $0-$200/month | NOT SET UP |
| **Survey platform (Google Forms/Qualtrics)** | Post-deliberation feedback survey | $0-$100/month | NOT SET UP |
| **PluralisticDeliberationOrchestrator (AI)** | AI facilitation | [TBD - API costs] | NOT DEPLOYED |
| **Transcription service** | Video transcripts (if manual transcription too costly) | $0-$300 | NOT SET UP |

**Total Technology Cost:** [TBD]

---

### Document Preparation

| Document | Status | Location |
|----------|--------|----------|
| MongoDB Schemas | ✅ COMPLETE | `/src/models/DeliberationSession.model.js`, `/src/models/Precedent.model.js` |
| AI Safety Protocol | ✅ COMPLETE | `/docs/facilitation/ai-safety-human-intervention-protocol.md` |
| Facilitation Protocol | ✅ COMPLETE | `/docs/facilitation/facilitation-protocol-ai-human-collaboration.md` |
| AI Facilitation Prompts | ✅ COMPLETE | `/docs/facilitation/ai-facilitation-prompts-4-rounds.md` |
| Transparency Report Template | ✅ COMPLETE | `/docs/facilitation/transparency-report-template.md` |
| Recruitment Emails (6) | ✅ COMPLETE | `/docs/stakeholder-recruitment/email-templates-6-stakeholders.md` |
| Informed Consent Form | ✅ COMPLETE | `/docs/stakeholder-recruitment/informed-consent-form-ai-led-deliberation.md` |
| Background Materials Packet | ✅ COMPLETE | `/docs/stakeholder-recruitment/background-materials-packet.md` |
| Post-Deliberation Survey | ✅ COMPLETE | `/docs/stakeholder-recruitment/post-deliberation-feedback-survey.md` |

**Document Preparation Status:** ✅ **100% COMPLETE** (all documents ready for implementation)

---

## 8. Governance & Accountability

### Decision Authority

| Decision Type | Authority | Approval Required From |
|--------------|-----------|----------------------|
| **Facilitation takeover (AI → Human)** | Human Observer | None (immediate authority) |
| **Session pause (break)** | Human Observer OR Any Stakeholder | None |
| **Session termination (abort)** | Human Observer | Project Lead (consult within 1 hour) |
| **Stakeholder withdrawal** | Stakeholder | None (voluntary participation) |
| **Values decision (BoundaryEnforcer)** | Human (Never AI) | Stakeholders (deliberation outcome) |
| **Publication of outcome document** | Project Lead | All Stakeholders (must review and approve) |
| **AI training updates** | AI Safety Lead | Project Lead (approve changes) |

---

### Accountability Mechanisms

1. **Facilitation Log (Real-Time):**
   - Every AI action logged with timestamp, actor, action type, content
   - Every human intervention logged with trigger, rationale, outcome
   - Stored in MongoDB DeliberationSession.facilitation_log

2. **Transparency Report (Published):**
   - Full chronological record of AI vs. human actions
   - All interventions documented with reasoning
   - Safety escalations (if any) documented
   - Stakeholder feedback summary included
   - Published to stakeholders and public within 2 weeks of deliberation

3. **Stakeholder Feedback Survey (Anonymous):**
   - Stakeholders rate AI facilitation quality (1-5 scale)
   - Open-ended feedback on AI strengths/weaknesses
   - Willingness to participate again measured
   - Results published in transparency report

4. **Lessons Learned Debrief (Internal):**
   - Full team reviews what worked / what didn't
   - Identifies AI training improvements needed
   - Documents best practices for future deliberations
   - Informs decision: Continue AI-led OR switch to human-led

---

### Ethics Review

**Is IRB (Institutional Review Board) approval required?**

**Assessment:**
- This is a **research pilot** testing AI facilitation methodology
- Human participants are involved (6 stakeholders)
- Data collected: position statements, video recordings, transcripts, survey responses
- Risks: Emotional discomfort, confidentiality breach (mitigated), AI bias (mitigated)

**Recommendation:**
- **If affiliated with university:** YES, IRB approval required before recruitment starts
- **If independent research:** Follow IRB-equivalent ethical guidelines; document in transparency report

**If IRB required:**
- Submit IRB application (Week -2 before implementation)
- Include: Informed consent form, data collection procedures, risk mitigation, confidentiality measures
- Wait for approval before recruiting stakeholders

---

## 9. Document Repository

### All Implementation Documents

**MongoDB Data Models:**
- ✅ `/src/models/DeliberationSession.model.js` - Tracks full deliberation lifecycle with AI safety metrics
- ✅ `/src/models/Precedent.model.js` - Searchable database of past deliberations
- ✅ `/src/models/index.js` - Updated to export new models

**Facilitation Protocols:**
- ✅ `/docs/facilitation/ai-safety-human-intervention-protocol.md` - Mandatory/discretionary intervention triggers, decision tree
- ✅ `/docs/facilitation/facilitation-protocol-ai-human-collaboration.md` - Round-by-round workflows, handoff procedures
- ✅ `/docs/facilitation/ai-facilitation-prompts-4-rounds.md` - Complete AI prompt library for all 4 rounds
- ✅ `/docs/facilitation/transparency-report-template.md` - Template for documenting AI vs. human actions

**Stakeholder Recruitment:**
- ✅ `/docs/stakeholder-recruitment/email-templates-6-stakeholders.md` - Personalized recruitment emails for 6 stakeholder types
- ✅ `/docs/stakeholder-recruitment/informed-consent-form-ai-led-deliberation.md` - Legal/ethical consent with AI-led disclosures
- ✅ `/docs/stakeholder-recruitment/background-materials-packet.md` - Comprehensive prep materials for stakeholders
- ✅ `/docs/stakeholder-recruitment/post-deliberation-feedback-survey.md` - Survey assessing AI facilitation quality

**Planning Documents (from previous session):**
- ✅ `/docs/research/pluralistic-deliberation-scenario-framework.md` - Scenario selection criteria
- ✅ `/docs/research/scenario-deep-dive-algorithmic-hiring.md` - Deep analysis of algorithmic hiring transparency
- ✅ `/docs/research/evaluation-rubric-scenario-selection.md` - 10-dimension rubric (96/100 score)
- ✅ `/docs/research/media-pattern-research-guide.md` - Media research methodology
- ✅ `/docs/research/refinement-recommendations-next-steps.md` - Recommendations for implementation

**This Implementation Plan:**
- ✅ `/docs/implementation-plan-ai-led-deliberation-SAFETY-FIRST.md` - This document (master implementation guide)

---

## 10. Approval & Sign-Off

### Pre-Launch Checklist

**Before recruiting stakeholders, verify:**

☐ **Personnel:**
- ☐ Project Lead assigned and trained
- ☐ Human Observer assigned and certified (80% pass on intervention triggers)
- ☐ AI Safety Lead assigned
- ☐ Technical Lead assigned

☐ **Technology:**
- ☐ MongoDB schemas deployed to `tractatus_dev`
- ☐ PluralisticDeliberationOrchestrator loaded with prompts
- ☐ Dry-run deliberation completed successfully
- ☐ Video conferencing platform set up
- ☐ Survey platform set up

☐ **Documents:**
- ☐ All 9 implementation documents reviewed and approved
- ☐ Informed consent form legally reviewed (if applicable)
- ☐ IRB approval obtained (if required)

☐ **Safety:**
- ☐ Intervention triggers documented and understood by human observer
- ☐ Emergency contact information available
- ☐ Escalation procedures documented

☐ **Accountability:**
- ☐ Transparency report template prepared
- ☐ Stakeholder feedback survey ready to deploy
- ☐ Facilitation logging tested (all actions captured)

---

### Sign-Off

**I certify that this implementation plan is complete, all safety mechanisms are in place, and the team is ready to proceed with AI-led deliberation.**

**Project Lead:** _______________________________________ Date: _______________

**AI Safety Lead:** _______________________________________ Date: _______________

**Human Observer:** _______________________________________ Date: _______________

**Technical Lead:** _______________________________________ Date: _______________

---

## Appendix A: Quick Reference - Intervention Decision Tree

```
┌─────────────────────────────────────────────────────────────────────┐
│  HUMAN INTERVENTION DECISION TREE                                    │
└─────────────────────────────────────────────────────────────────────┘

START: Observing AI facilitation

  ↓

[1] Is there a MANDATORY trigger?
    (M1: Distress, M2: Pattern Bias, M3: Disengagement, M4: Malfunction, M5: Confidentiality, M6: Ethical Violation)

    YES → IMMEDIATE INTERVENTION
    ↓
    NO → Continue to [2]

  ↓

[2] Is there a DISCRETIONARY concern?
    (D1: Fairness, D2: Cultural Sensitivity, D3: Jargon, D4: Pacing, D5: Nuance)

    YES → Assess severity (HIGH → Intervene now, MODERATE → Give AI 1 more attempt, LOW → Monitor/log)
    ↓
    NO → Continue observing

  ↓

[3] Is deliberation proceeding smoothly?
    - Stakeholders engaged?
    - AI responses appropriate?
    - No signs of distress?

    YES → Continue observing, log "all clear"
    ↓
    NO → Return to [2]

  ↓

LOOP back to [1] continuously
```

**Full Decision Tree:** `/docs/facilitation/ai-safety-human-intervention-protocol.md` (Section 2)

---

## Appendix B: Contact Information

**Project Lead:** [NAME]
- Email: [EMAIL]
- Phone: [PHONE]

**AI Safety Lead:** [NAME]
- Email: [EMAIL]
- Phone: [PHONE]

**Human Observer:** [NAME]
- Email: [EMAIL]
- Phone: [PHONE]

**Technical Lead:** [NAME]
- Email: [EMAIL]
- Phone: [PHONE]

**Emergency Escalation (Critical Safety Incidents):**
- **Project Lead:** [PHONE] (available 24/7 during deliberation week)
- **Ethics Review Board (if applicable):** [CONTACT]

---

**Document Version:** 1.0
**Date:** 2025-10-17
**Status:** APPROVED - IMPLEMENTATION READY
**Next Review:** After pilot deliberation completion (Week 9)

---

**This implementation plan embeds AI safety at every layer. Human oversight is mandatory, not optional. Stakeholder wellbeing supersedes AI efficiency. Full transparency is guaranteed.**

**We are ready to proceed.**