tractatus/public/locales/en/leader.json

{
  "page": {
    "title": "For Decision-Makers | Tractatus AI Safety Framework",
    "description": "Structural AI governance for organisations deploying LLM systems at scale. Research framework addressing architectural gaps in AI safety."
  },
  "header": {
    "badge": "Research Framework • Early Development",
    "title": "Tractatus: Architectural Governance for LLM Systems",
    "subtitle": "A governance framework addressing structural gaps in AI safety through external architectural controls. Designed for organisations deploying large language models at scale where conventional oversight mechanisms prove insufficient."
  },
  "sections": {
    "governance_gap": {
      "heading": "The Governance Gap",
      "intro": "Current AI governance approaches—policy documents, training programmes, ethical guidelines—rely on voluntary compliance. LLM systems can bypass these controls simply by not invoking them. When an AI agent needs to check a policy, it must choose to do so. When it should escalate a decision to human oversight, it must recognise that obligation.",
      "problem": "This creates a structural problem: governance exists only insofar as the AI acknowledges it. For organisations subject to EU AI Act Article 14 (human oversight requirements) or deploying AI in high-stakes domains, this voluntary model is inadequate.",
      "solution": "Tractatus explores whether governance can be made architecturally external—difficult to bypass not through better prompts, but through system design that places control points outside the AI's discretion."
    },
    "architectural_approach": {
      "heading": "Architectural Approach",
      "three_layer_title": "Three-Layer Architecture",
      "services_title": "Six Governance Services",
      "arch_layers": {
        "layer_1_title": "Agent Runtime Layer",
        "layer_1_desc": "Any LLM system (Claude Code, Copilot, custom agents, LangChain, CrewAI). The AI system being governed.",
        "layer_2_title": "Governance Layer",
        "layer_2_desc": "Six autonomous services that intercept, validate, and document AI operations. External to the AI runtime.",
        "layer_3_title": "Persistent Storage Layer",
        "layer_3_desc": "Immutable audit logs, governance rules, instruction history. Cannot be altered by AI prompts."
      },
      "services": {
        "service_1_title": "BoundaryEnforcer",
        "service_1_desc": "Blocks AI from making values decisions without human approval. Enforces decision boundaries through architectural controls.",
        "service_2_title": "InstructionPersistenceClassifier",
        "service_2_desc": "Prevents pattern bias from overriding explicit instructions. Stores organisational directives external to AI context.",
        "service_3_title": "CrossReferenceValidator",
        "service_3_desc": "Validates AI actions against stored policies before execution. Detects conflicts with established rules.",
        "service_4_title": "ContextPressureMonitor",
        "service_4_desc": "Tracks session complexity, token usage, conversation length. Detects degradation in decision quality.",
        "service_5_title": "MetacognitiveVerifier",
        "service_5_desc": "Validates reasoning quality before complex operations. Self-checks alignment, coherence, alternatives.",
        "service_6_title": "PluralisticDeliberationOrchestrator",
        "service_6_desc": "Facilitates multi-stakeholder deliberation for values conflicts. Non-hierarchical engagement with documented dissent."
      }
    },
    "governance_capabilities": {
      "heading": "Governance Capabilities",
      "intro": "Three interactive demonstrations showing governance infrastructure in operation. These show mechanisms, not fictional scenarios.",
      "audit_trail_title": "Audit Trail & Compliance Evidence Generation",
      "audit_trail_desc": "Immutable logging, evidence extraction, regulatory reporting",
      "continuous_improvement_title": "Continuous Improvement: Incident → Rule Creation",
      "continuous_improvement_desc": "Learning from failures, automated rule generation, validation",
      "pluralistic_deliberation_title": "Pluralistic Deliberation: Values Conflict Resolution",
      "pluralistic_deliberation_desc": "Multi-stakeholder engagement, non-hierarchical process, moral remainder documentation",
      "sample_heading": "Sample Audit Log Structure",
      "immutability_label": "Immutability:",
      "immutability_text": "Audit logs stored in append-only database. AI cannot modify or delete entries.",
      "compliance_label": "Compliance Evidence:",
      "compliance_text": "Automatic tagging with regulatory requirements (EU AI Act Article 14, GDPR Article 22, etc.)",
      "export_label": "Export Capabilities:",
      "export_text": "Generate compliance reports for regulators showing human oversight enforcement",
      "footer_text": "When regulator asks How do you prove effective human oversight at scale, this audit trail provides structural evidence independent of AI cooperation.",
      "flow_heading": "Incident Learning Flow",
      "step_1_desc": "CrossReferenceValidator flags policy violation",
      "step_2_desc": "Automated analysis of instruction history, context state",
      "step_3_desc": "Proposed governance rule to prevent recurrence",
      "step_4_desc": "Governance board reviews and approves new rule",
      "step_5_desc": "Rule added to persistent storage, active immediately",
      "example_heading": "Example Generated Rule",
      "learning_label": "Organisational Learning:",
      "learning_text": "When one team encounters governance failure, entire organisation benefits from automatically generated preventive rules. Scales governance knowledge without manual documentation.",
      "conflict_label": "Conflict Detection:",
      "conflict_text": "AI system identifies competing values in decision context (e.g., efficiency vs. transparency, cost vs. risk mitigation, innovation vs. regulatory compliance). BoundaryEnforcer blocks autonomous decision, escalates to PluralisticDeliberationOrchestrator.",
      "stakeholder_heading": "Stakeholder Identification Process",
      "stakeholder_1": "Automatic Detection: System identifies which values frameworks are in tension (utilitarian, deontological, virtue ethics, contractarian, etc.)",
      "stakeholder_2": "Stakeholder Mapping: Identifies parties with legitimate interest in decision (affected parties, domain experts, governance authorities, community representatives)",
      "stakeholder_3": "Human Approval: Governance board reviews stakeholder list, adds/removes as appropriate (TRA-OPS-0002)",
      "deliberation_heading": "Non-Hierarchical Deliberation",
      "equal_voice_title": "Equal Voice",
      "equal_voice_text": "All stakeholders present perspectives without hierarchical weighting. Technical experts do not automatically override community concerns.",
      "dissent_title": "Documented Dissent",
      "dissent_text": "Minority positions recorded in full. Dissenting stakeholders can document why consensus fails their values framework.",
      "moral_title": "Moral Remainder",
      "moral_text": "System documents unavoidable value trade-offs. Even correct decision creates documented harm to other legitimate values.",
      "precedent_title": "Precedent (Not Binding)",
      "precedent_text": "Decision becomes informative precedent for similar conflicts. But context differences mean precedents guide, not dictate.",
      "record_heading": "Deliberation Record Structure",
      "key_principle": "Key Principle: When legitimate values conflict, no algorithm can determine the correct answer. Tractatus ensures decisions are made through inclusive deliberation with full documentation of trade-offs, rather than AI imposing single values framework or decision-maker dismissing stakeholder concerns."
    },
    "development_status": {
      "heading": "Development Status",
      "warning_title": "Early-Stage Research Framework",
      "warning_text": "Tractatus is a proof-of-concept developed over six months in a single project context (this website). It demonstrates architectural patterns for AI governance but has not undergone independent validation, red-team testing, or multi-organisation deployment.",
      "validation_title": "Validated vs. Not Validated",
      "validated_label": "Validated:",
      "validated_text": "Framework successfully governs Claude Code in development workflows. User reports order-of-magnitude improvement in productivity for non-technical operators building production systems.",
      "not_validated_label": "Not Validated:",
      "not_validated_text": "Performance at enterprise scale, integration complexity with existing systems, effectiveness against adversarial prompts, cross-platform consistency.",
      "limitation_label": "Known Limitation:",
      "limitation_text": "Framework can be bypassed if AI simply chooses not to use governance tools. Voluntary invocation remains a structural weakness requiring external enforcement mechanisms."
    },
    "eu_ai_act": {
      "heading": "EU AI Act Considerations",
      "article_14_title": "Regulation 2024/1689, Article 14: Human Oversight",
      "intro": "The EU AI Act (Regulation 2024/1689) establishes human oversight requirements for high-risk AI systems (Article 14). Organisations must ensure AI systems are effectively overseen by natural persons with authority to interrupt or disregard AI outputs.",
      "addresses": "Tractatus addresses this through architectural controls that:",
      "bullet_1": "Generate immutable audit trails documenting AI decision-making processes",
      "bullet_2": "Enforce human approval requirements for values-based decisions",
      "bullet_3": "Provide evidence of oversight mechanisms independent of AI cooperation",
      "bullet_4": "Document compliance with transparency and record-keeping obligations",
      "disclaimer": "This does not constitute legal compliance advice. Organisations should evaluate whether these architectural patterns align with their specific regulatory obligations in consultation with legal counsel.",
      "penalties": "Maximum penalties under EU AI Act: 35 million euros or 7 percent of global annual turnover (whichever is higher) for prohibited AI practices; 15 million euros or 3 percent for other violations."
    },
    "research_foundations": {
      "heading": "Research Foundations",
      "org_theory_title": "Organisational Theory & Philosophical Basis",
      "intro": "Tractatus draws on 40+ years of organisational theory research: time-based organisation (Bluedorn, Ancona), knowledge orchestration (Crossan), post-bureaucratic authority (Laloux), structural inertia (Hannan Freeman).",
      "premise": "Core premise: When knowledge becomes ubiquitous through AI, authority must derive from appropriate time horizon and domain expertise rather than hierarchical position. Governance systems must orchestrate decision-making across strategic, operational, and tactical timescales.",
      "view_pdf": "View complete organisational theory foundations (PDF)",
      "ai_safety_title": "AI Safety Research: Architectural Safeguards Against LLM Hierarchical Dominance",
      "ai_safety_desc": "How Tractatus protects pluralistic values from AI pattern bias while maintaining safety boundaries.",
      "pdf_link": "PDF",
      "read_online": "Read online"
    },
    "scope_limitations": {
      "heading": "Scope & Limitations",
      "title": "What This Is Not • What It Offers",
      "not_title": "Tractatus is not:",
      "offers_title": "What it offers:",
      "not_1": "A comprehensive AI safety solution",
      "not_2": "Independently validated or security-audited",
      "not_3": "Tested against adversarial attacks",
      "not_4": "Proven effective across multiple organisations",
      "not_5": "A substitute for legal compliance review",
      "not_6": "A commercial product (research framework, Apache 2.0 licence)",
      "offers_1": "Architectural patterns for external governance controls",
      "offers_2": "Reference implementation demonstrating feasibility",
      "offers_3": "Foundation for organisational pilots and validation studies",
      "offers_4": "Evidence that structural approaches to AI safety merit investigation"
    }
  }
}