Adhere to OECD AI Principles Section 1.3 (Transparency and explainability). AI actors should commit to (a) transparency and responsible disclosure regarding AI systems to foster understanding of AI systems + make stakeholders aware of their interactions with AI systems including in the workplace + provide meaningful information appropriate to the context and consistent with the state of the art to those adversely affected by an AI system + enable those adversely affected by an AI system to challenge its output based on plain and easy-to-understand information on the factors and the logic that served as the basis for the prediction + recommendation + or decision. Implement (a) explainability requirements proportionate to risk and impact level with documented explainability capability per AI system, (b) algorithmic transparency measures including system documentation + model cards + datasheets + data nutrition labels, (c) user notification of AI interactions per applicable jurisdiction requirement (EU AI Act Article 50 + similar), (d) public-facing disclosure of AI use per applicable governance + regulatory + commitment regime, (e) accessibility of disclosure (multiple languages + plain language + alternative formats), (f) AI system documentation per applicable regulatory regimes (EU AI Act Annex IV + ISO/IEC 42001 + NIST AI RMF documentation).
This control maps to 21 controls across 9 other frameworks. If you already hold one of them, the evidence you collected for it is the starting point here rather than new work.
Every mapping shown was judged rather than inferred from wording similarity, and the ones that failed review are published too. See the coverage reports and what was rejected.
The graph holds this control, the 21 it maps to, and the evidence behind each claim, over MCP and REST.