The principle "Principles for the Stewardship of AI Applications" has mentioned the topic "transparency" in the following places:

    3. Scientific Integrity and Information Quality

    Agencies should hold information, whether produced by the government or acquired by the government from third parties, that is likely to have a clear and substantial influence on important public policy or private sector decisions (including those made by consumers) to a high standard of quality, transparency, and compliance.

    3. Scientific Integrity and Information Quality

    Best practices include transparently articulating the strengths, weaknesses, intended optimizations or outcomes, bias mitigation, and appropriate uses of the AI application’s results.

    4. Risk Assessment and Management

    Agencies should be transparent about their evaluations of risk and re evaluate their assumptions and conclusions at appropriate intervals so as to foster accountability.

    7. Fairness and Non Discrimination

    Agencies should consider in a transparent manner the impacts that AI applications may have on discrimination.

    8. Disclosure and Transparency

    Disclosure and transparency

    8. Disclosure and Transparency

    In addition to improving the rulemaking process, transparency and disclosure can increase public trust and confidence in AI applications.

    8. Disclosure and Transparency

    Agencies should carefully consider the sufficiency of existing or evolving legal, policy, and regulatory environments before contemplating additional measures for disclosure and transparency.

    8. Disclosure and Transparency

    What constitutes appropriate disclosure and transparency is context specific, depending on assessments of potential harms, the magnitude of those harms, the technical state of the art, and the potential benefits of the AI application.