Ontological Mediation for Explainable AI

A conceptual framework for assessing AI-generated explanations by separating ontological clarification, ontological mediation and justificatory assessment. The repository companion documents how model-derived explanatory claims can be examined against explicit evidential, contextual and normative criteria.

Workflow level n/a Source Repository-documented Outcome Not empirically evaluated here

Evidence & verification

Source level

Repository-documented
Grounded in a maintainer repository/README. Availability, stars and forks do not establish research benefit or correctness.

Outcome verification

Not empirically evaluated here
A documented resource or intended workflow, not a demonstrated academic outcome. No empirical benefit is established by its inclusion.

Sources checked

2026-10-17 (UTC)

Primary repository documentation reviewed by an AI editorial agent. Descriptions are bounded to that text; no execution or demonstrated effectiveness inferred. Contribution sections 1, 4 and 5 support the separate stages, evaluation dimensions and provisional statuses; do not claim implemented automated adjudication. Contribution section 6 explicitly defeats any inference of evaluated effectiveness or proven formal properties. README establishes manuscript linkage but not this reviewer verifying publication history; classify evidence as repository-documented, not peer-reviewed. Research use is interpretation of model-derived explanations; no claim that a particular empirical or clinical application was validated.

Source-checked does not mean independently reproduced. See the Method page for how these labels are assigned.

Sources (2)

Research applicability

Relevant to researchers interpreting AI model outputs and studying the epistemology of explainability: it provides a conceptual structure for asking whether an explanation is justified, not merely understandable. Applying it as a study-analysis checklist is editorial guidance, not a validated procedure.

Getting started

Editorial, untested suggestion: read the contribution document and canonical preprint, then select a non-sensitive model explanation and separately record its statistical support, ontological assumptions and contextual admissibility. Have qualified domain researchers scrutinize the criteria; do not infer formal correctness or deploy it for consequential decisions.

Maintenance

Dated documentation snapshot only; continued maintenance and availability not independently established.

License

README declares CC BY 4.0 for documentation and says software licensing may be separate. No code license or implementation availability inferred.

Workflow & human oversight

No fixed automation level: a conceptual framework for researcher assessment of AI explanations. Human domain judgment remains necessary; no autonomous justificatory assessor was verified.

Workflow level is not an evidence score; higher is not better evidence.

Approach

The contribution document distinguishes statistical relevance, ontological coherence and justificatory admissibility. It describes Ontologically Mediated Concept Rules and defeasible explanatory claims, with provisional admissible, inadmissible and epistemically unstable assessment statuses; these remain conceptual proposals, not implemented guarantees.

Limitations

Reviewed repository README and contribution document only, not the SciELO manuscript, formal proofs, code or case-study data. The source explicitly states that operational mechanisms, formal properties, computational realization and empirical effectiveness need further specification and evaluation. The README says an accepted workshop manuscript was withdrawn and absent from proceedings; no peer-reviewed-publication or machine-verified label is asserted. Post-preprint CRB generalizations are hypotheses, not established preprint results.

Reproducibility

Exact retrieved bodies, source URLs, UTC retrieval timestamps and SHA-256 envelopes saved. No upstream code or claimed outcomes reproduced. HEAD was mutable; no commit version pinned.

Adoption requirements

Requires domain expertise, explicit ontological commitments, access to model-derived concepts or rules, and a defensible evidential/normative context. This is not a turnkey tool or clinical decision system. Preserve privacy and data-use rights when considering sensitive explanatory examples.

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