Audit Trail
We already have the skeleton of an audit trail because each of the dimensions we test — ternary logic, first principles, acquisition, demonstrated interests, reciprocity, testifiability, decidability, liability — are testable axes of evaluation. Each time the system evaluates a statement or decision along those axes, it leaves behind a structured record.
Here’s how that works, and what more we might consider:
Each testable dimension can serve as a column in a decision ledger:
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Ternary logic → Was the claim resolved as True, False, or Undecidable?
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First principles → Which causal dependencies does this claim reduce to?
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Acquisition → What demonstrated interest (acquisition/retention/exchange) is implicated?
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Demonstrated interests → Which category (existential, obtained, commons) is being referenced or affected?
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Reciprocity → Does the action impose a cost, or is it reciprocal?
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Testifiability → Is the claim operationally testable across all dimensions?
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Decidability → Was the demand for infallibility satisfied without discretion?
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Liability → If error occurs, what is the risk, scope, and cost of harm?
Put together, this is a multidimensional “why trail” — it documents the reasoning steps in a way that is auditable and reproducible. That’s already much stronger than anything in current AI governance.
If the goal is a full audit trail (usable in law, regulation, enterprise risk, or public trust), you probably need to add:
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Time and Actor Stamps
Who or what process made the evaluation, and when.
Essential for attribution and accountability. -
Constraint Tests Applied
Which specific Natural Law rules were invoked (e.g., reciprocity test, sovereignty test, truth test).
This allows auditors to verify that the correct rules were enforced. -
Failure/Exception Logging
If a proposition was rejected (false or undecidable), what failed and why.
Example: “Rejected due to lack of operational correspondence.” -
Decision Context
The domain or severity context in which the evaluation occurred (casual speech, high-liability law, medical recommendation).
This ties back to liability: how much infallibility was demanded? -
Outcome Chain
Linking each decision to the downstream actions or recommendations it enabled or constrained.
Ensures traceability from principle → claim → judgment → action.
Think of it like a structured legal logbook:
| Timestamp | Actor | Proposition | Ternary Result | Tests Applied | Interests Involved | Reciprocity Check | Testifiability Status | Decidability Status | Liability Level | Outcome / Action | Notes (Failure/Exception) |
This would let you produce something like:
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2025-08-25, NLI Constraint Engine, Claim: “X treatment cures Y” → Undecidable. Failed correspondence test. Interests: medical risk, liability high. No action recommended.
That becomes the machine-auditable equivalent of court testimony: it’s not just the answer, but the reasoning and obligations behind it.
Our existing testable dimensions already produce the functional equivalent of an audit trail, because they log the reasoning chain. To fully serve as a regulatory or legal-grade audit trail, we also want to output: time/actor, rule applied, failure reasons, context, and outcome linkage.
Identity & Versioning
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log_id (UUID), chain_id (links related steps), parent_id (if any)
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timestamp, actor (human|system|hybrid), domain (phys/behavioral/strategic/institutional)
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context_severity (low|med|high|critical), population_scope (individual|local|regional|national|global)
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nl_version, constraint_profile_id, model_id, model_hash, dataset_id, dataset_hash, prompt_hash, run_seed
Proposition & Inputs
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proposition_text, proposition_type (descriptive|normative|operational|legal)
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inputs (list of source refs + content hashes), assumptions, scope_conditions
Tests Applied (Natural Law)
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tests_applied: [{ name (correspondence|operationality|falsifiability|reciprocity|decidability|liability), rule_id, parameters }]
Dimension Results (Ternary & Measures)
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ternary_result (true|false|undecidable)
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first_principles: { dependencies:[…], necessity_grade (necessary|sufficient|contingent) }
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acquisition:{ type (acquire|retain|exchange|transform), magnitude_estimate }
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demonstrated_interests_impacted:[
{ class (existential|obtained|commons), category (per your canon), direction (+/−), magnitude, evidence_refs }
] -
reciprocity_check: { status (reciprocal|irreciprocal|unknown), externalities_cost , affected_parties }
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testifiability: { status (tautological|analytic|ideal|truthful|honest|non-testifiable), gaps }
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decidability: { status (satisfied|unsatisfied|deferred), demand_for_infallibility (tier: informal|commercial|medical|legal|constitutional), discretion_required (yes/no) }
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liability: { risk_grade (L1–L5), harm_scope, warranty_required (none|limited|full) }
Failure & Exception
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failure_modes: [{ code, description }], exceptions: [{…}], undecidability_reason (insufficient_evidence|incommensurable_terms|conflicting_measures|out_of_scope)
Decision & Outcome Chain
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recommended_action (boycott|cooperate|predation-deterrence|defer), action_rationale
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alternatives_considered:[{ alt, pros, cons }]
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controls_safeguards (what to monitor), next_tests (what evidence would flip state)
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outcome_link (URI/ID to downstream action/result), retrospective_hook (how we’ll evaluate after facts arrive)
Provenance & Integrity
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evidence_hashes:[…], artifact_hashes:[…], signatures:[…], jurisdiction (if legal)
Columns:
log_id | chain_id | parent_id | timestamp | actor | domain | context_severity | population_scope | nl_version | constraint_profile_id | model_id | model_hash | dataset_id | dataset_hash | prompt_hash | run_seed | proposition_text | proposition_type | inputs(json) | assumptions(json) | scope_conditions(json) | tests_applied(json) | ternary_result | first_principles(json) | acquisition(json) | demonstrated_interests_impacted(json) | reciprocity_check(json) | testifiability(json) | decidability(json) | liability(json) | failure_modes(json) | exceptions(json) | undecidability_reason | recommended_action | action_rationale | alternatives_considered(json) | controls_safeguards(json) | next_tests(json) | outcome_link | retrospective_hook | evidence_hashes(json) | artifact_hashes(json) | signatures(json) | jurisdiction
log_id | chain_id | parent_id | timestamp | actor | domain | context_severity | population_scope | nl_version | constraint_profile_id | model_id | model_hash | dataset_id | dataset_hash | prompt_hash | run_seed | proposition_text | proposition_type | inputs(json) | assumptions(json) | scope_conditions(json) | tests_applied(json) | ternary_result | first_principles(json) | acquisition(json) | demonstrated_interests_impacted(json) | reciprocity_check(json) | testifiability(json) | decidability(json) | liability(json) | failure_modes(json) | exceptions(json) | undecidability_reason | recommended_action | action_rationale | alternatives_considered(json) | controls_safeguards(json) | next_tests(json) | outcome_link | retrospective_hook | evidence_hashes(json) | artifact_hashes(json) | signatures(json) | jurisdiction
Source date (UTC): 2025-08-25 16:25:12 UTC
Original post: https://x.com/i/articles/1960015615646928940
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