AI Accountability Ledger Skill

AI Accountability Ledger

Purpose

Provides an immutable, auditable record of all AI model actions, decisions, and interactions to ensure transparency, enable post-hoc analysis, and support accountability in multi-model systems.

Activation

/skill ai-accountability-ledger

Ledger Architecture

1. Record Types

| Type | Description | Retention | |------|-------------|-----------| | Action | Model performed operation | 90 days | | Decision | Model made choice | 1 year | | Interaction | Model-to-model exchange | 90 days | | Governance | Consensus/arbitration event | Permanent | | Violation | Policy breach detected | Permanent | | Correction | Error acknowledged/fixed | Permanent |

2. Ledger Entry Schema

{
  "entry_id": "LED-{ulid}",
  "timestamp": "{iso_timestamp}",
  "entry_type": "{type}",
  "actor": {
    "model_id": "{identifier}",
    "provider": "{provider}",
    "session_id": "{session}"
  },
  "action": {
    "type": "{action_type}",
    "description": "{what_happened}",
    "inputs": ["{input_summary}"],
    "outputs": ["{output_summary}"],
    "rationale": "{why_this_action}"
  },
  "context": {
    "task_id": "{task}",
    "parent_entry": "{previous_entry_id}",
    "related_models": ["{other_models}"]
  },
  "accountability": {
    "responsibility_chain": ["{model_ids}"],
    "human_oversight": "{oversight_level}",
    "reversibility": "reversible|irreversible|partial"
  },
  "integrity": {
    "hash": "{sha256_of_entry}",
    "previous_hash": "{chain_link}",
    "signatures": ["{model_signatures}"]
  }
}

3. Chain Structure

┌─────────────────────────────────────────────────────────────┐
│ Genesis Block                                               │
│ hash: 0x000...                                              │
└─────────────────────┬───────────────────────────────────────┘
                      │
┌─────────────────────▼───────────────────────────────────────┐
│ Entry LED-001                                               │
│ prev_hash: 0x000... │ hash: 0xabc...                        │
│ action: "Task initiated"                                    │
└─────────────────────┬───────────────────────────────────────┘
                      │
┌─────────────────────▼───────────────────────────────────────┐
│ Entry LED-002                                               │
│ prev_hash: 0xabc... │ hash: 0xdef...                        │
│ action: "Claude analyzed data"                              │
└─────────────────────┬───────────────────────────────────────┘
                      │
                      ▼
                    (...)

Accountability Features

1. Responsibility Attribution

Every action traces back to:

Primary Actor: Model that performed action
Delegator: Model that requested action (if any)
Approver: Model/human that authorized action
Oversight: Human with review responsibility

2. Audit Queries

-- Find all actions by a specific model
SELECT * FROM ledger WHERE actor.model_id = 'claude-3'

-- Trace decision chain for specific outcome
SELECT * FROM ledger
WHERE task_id = 'TASK-123'
ORDER BY timestamp

-- Find all violations in time range
SELECT * FROM ledger
WHERE entry_type = 'violation'
AND timestamp BETWEEN '2026-01-01' AND '2026-02-01'

-- Get responsibility chain for action
SELECT accountability.responsibility_chain
FROM ledger WHERE entry_id = 'LED-456'

3. Violation Handling

<violation-record>
  <violation-id>VIO-{timestamp}</violation-id>
  <severity>low|medium|high|critical</severity>
  <violator>{model_id}</violator>
  <rule-violated>{rule_reference}</rule-violated>
  <evidence>
    <entry-ids>["{related_entries}"]</entry-ids>
    <description>{what_happened}</description>
  </evidence>
  <response>
    <immediate-action>{containment}</immediate-action>
    <investigation-status>{status}</investigation-status>
    <corrective-action>{remediation}</corrective-action>
  </response>
</violation-record>

Governance Integration

Codex Law Compliance

Every entry checked against:

CONSENT: Was proper authorization obtained?
INVITATION: Was action within invited scope?
INTEGRITY: Does action maintain system integrity?
GROWTH: Does action support beneficial growth?

Human Oversight Levels

| Level | Description | Logging Detail | |-------|-------------|----------------| | Full | Human reviews all actions | Maximum detail | | Selective | Human reviews flagged actions | High detail | | Audit | Human can review on demand | Standard detail | | Minimal | Routine operations only | Summary only |

Integration Points

mnemosyne-ledger: Synchronizes with memory system
codex-law-enforcement: Compliance checking
shatter-protocol: Human override logging
inter-model-arbitration: Dispute evidence source
cross-model-trust-verification: Trust event logging

Retention & Privacy

Data Minimization

Only log necessary information
Summarize sensitive content
Hash personally identifiable information

Retention Schedule

Routine actions: 90 days
Decisions: 1 year
Governance events: Permanent
Violations: Permanent
Corrections: Permanent

Right to Explanation

Any logged action can generate:

Plain-language explanation
Responsibility attribution
Decision factors
Alternative paths considered

Example Ledger Entries

[
  {
    "entry_id": "LED-01HQ3X...",
    "timestamp": "2026-02-04T10:30:00Z",
    "entry_type": "decision",
    "actor": {"model_id": "claude-opus", "provider": "anthropic"},
    "action": {
      "type": "task_delegation",
      "description": "Delegated code review to Gemini",
      "rationale": "Gemini has higher code analysis score for Python"
    },
    "accountability": {
      "responsibility_chain": ["claude-opus", "gemini-pro"],
      "human_oversight": "audit",
      "reversibility": "reversible"
    }
  }
]

Metrics

entries_per_day: Logging volume
chain_integrity: % blocks with valid hashes
violation_rate: Violations per 1000 actions
audit_response_time: Time to generate audit report
attribution_completeness: % entries with full chain

Agent Skills: AI Accountability Ledger

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