Unlocking AI Transparency: The Access Methodology Framework for Classifying AI System Openness

Citation: Michele Herman (2025) Unlocking AI Transparency: The Access Methodology Framework for Classifying AI System Openness.
DOI (original publisher): 10.2139/SSRN.5152494
Semantic Scholar (metadata): 10.2139/SSRN.5152494
Sci-Hub (fulltext): 10.2139/SSRN.5152494
Internet Archive Scholar (search for fulltext): Unlocking AI Transparency: The Access Methodology Framework for Classifying AI System Openness
Wikidata (metadata): Q135644161
Download: https://ssrn.com/abstract=5152494
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Summary

Positions current “openness” schemes (e.g., MOF, OSAID-inspired approaches) as equating transparency with open-source licensing of components, then introduces an Access Methodology Framework (AMF) that classifies openness by the kind of access granted for evaluation rather than by redistribution rights. AMF prioritizes black-box conformity testing where feasible; when artifact access is needed, it requires licenses that permit testing/analysis to understand system behavior but needn’t grant reuse or commercial rights. It operationalizes two licensing modalities—Private Access (PA) under confidentiality for defined stakeholders and Open Access (OA) without confidentiality—allowing terms (e.g., no redistribution/commerce) tailored to risk and use case.

Theoretical and Practical Relevance

Provides a compliance and auditability path that decouples “transparency” from mandatory open-source release, offering regulators, platforms, and developers a use-case-specific access design (black-box tests first; PA/OA when necessary) that can preserve trade secrets while enabling scrutiny. As a complement (and counterpoint) to MOF/OSAID-style regimes, AMF gives institutions a way to specify testing rights, stakeholder scope, and confidentiality conditions without over- or under-opening components—potentially reducing open-washing debates while supporting innovation and risk mitigation.