Was thinking what could practically be done to assist when presnted with material which we dont know is AI/AGI as it gets smarter, or when we dont have access to adequate empirically or adequate literature to assess the material / and or relaity being presnted?
Could we kick of discussion on how we could do this. Suggesting some kind of audit and logic protocol.
Am not married in any way to this yoke below, its a conversation starter only, so please kick it, pull it and do whatever. Hopefully something more sophisticated, developed/made simple by collective genius/ for practical everyday usage. It might be enhanced by use of a spiritual/consciousness discernent aspect / principles?
I foresee real issues ahead if we mere mortals cannot engage/take on the machines to challenge and root out their AI/AGI slop/perceptual manipulations?.
Epistemic / Ontological AI/AGI Audit Template
Document Type: Review Checklist and Risk Assessment
Audit Checklist
Date:
Material Reviewed: Title / Date/ AI Source
1. Claim Identification
- All distinct claims have been separated.
- Each claim is labelled as descriptive, causal, normative, predictive, or definitional.
- Key terms have been extracted.
- *Hidden assumptions have been identified.
2. Conceptual Clarity
- Key terms are defined clearly.
- Terms are used consistently throughout.
- Ambiguous language has been flagged.
- Metaphor is not being presented as fact.
- Category errors have been checked.
3. Ontological Check
- The thing described is ontologically coherent.
- The claim refers to an entity, process, construct, or inference appropriately.
- Reification is not occurring.
- The existence claim is plausible as stated.
- The claim does not confuse label, construct, and reality.
4. Epistemic Check
- The basis for knowing is stated or inferable.
- The claim’s warrant matches its type.
- Observation, inference, theory, and speculation are not conflated.
- The conclusion does not exceed the available support.
- Possibility is not being treated as probability.
5. Logical Integrity
- No internal contradictions identified.
- No circular reasoning detected.
- No unsupported leaps in reasoning.
- No false dilemmas present.
- No equivocation in key terms.
- No non sequiturs identified.
6. Explanatory Adequacy
- The explanation accounts for the full issue.
- Alternative explanations were considered.
- The explanation is not unnecessarily complex.
- The explanation has clear scope and boundary conditions.
- The explanation adds more than it assumes.
7. Risk Assessment
- Likelihood has been scored.
- Impact has been scored.
- Risk score has been calculated.
- Risk rating band has been assigned.
- Escalation level matches the score.
- Residual risk has been considered where relevant.
8. Falsifiability and Testing
- It is clear what would disconfirm the claim.
- The claim can be wrong in principle.
- The claim is not so vague that it cannot be tested later.
- Observable indicators can be proposed for future validation.
- Boundary conditions are stated.
9. Provisional Rating
- High confidence — coherent, well-formed, and testable later.
- Moderate confidence — plausible but assumption-heavy.
- Low confidence — vague, weakly supported, or unstable.
- Reject — incoherent, contradictory, or ontologically unsound.
Decision Record
Primary conclusion:
Main weaknesses identified:
What would strengthen the claim later:
Follow-up empirical test needed:
Reviewer sign-off:
Scoring option:
Score each section from 0 to 2:
- 0 = not met
- 1 = partially met
- 2 = fully met
Total score: / 18
Interpretation:
- 15–18 = strong provisional acceptance
- 10–14 = cautious provisional acceptance
- 5–9 = weak / unresolved
- 0–4 = reject or suspend
Instructions and Definitions
How to use this checklist
Use this checklist when reviewing AI-generated, theoretical, or otherwise unverified material before empirical validation is possible. The purpose is to test whether the material is logically sound, conceptually coherent, and ontologically plausible before any direct evidence is available.
Claim type definitions
Descriptive claim
A descriptive claim states what is, was, or will be the case in observable reality. It reports a condition, pattern, or fact without explaining why it exists.
Example: “The register contains 42 active policies.”
Causal claim
A causal claim says that one thing produces, influences, or contributes to another. It explains why something happened or what effect something has.
Example: “Poor supervision caused the increase in incident reports.”
Normative claim
A normative claim says what should, ought to, or must happen according to a value, rule, or standard. It expresses judgment rather than observation.
Example: “The service should archive outdated procedures within 30 days.”
Predictive claim
A predictive claim says what is likely to happen in the future if current conditions continue or if a stated condition occurs. It projects an outcome.
Example: “If duplication is not addressed, staff will continue using inconsistent versions.”
Definitional claim
A definitional claim states what a term means, how a category is being used, or what counts as membership in a class. It sets boundaries around meaning rather than making a factual assertion.
Example: “For this review, ‘duplicate’ means any document that repeats the same process content under a different title.”
Review questions for each claim
- Is this describing reality, explaining causation, setting a norm, predicting an outcome, or defining a term?
- Does the claim match the kind of evidence or reasoning it requires?
- Is the claim being treated as a fact when it is actually a value judgment or definition?
- Is the claim clear enough to test later if empirical evidence becomes available?
Decision rule
- Descriptive claims need accuracy and observation.
- Causal claims need mechanism and support.
- Normative claims need values, standards, or policy basis.
- Predictive claims need conditions and likely outcomes.
- Definitional claims need clarity and consistency.