Direct answer
Public Trust, in this evidence assessment, is limited to the following inspected scope. Disclosure, meaningful information, and ability to challenge AI outcomes. Documentation, communication of impacts, limits, transparency, and accountability. The answer carries the source boundaries forward and does not infer authority from a neighboring topic.
Evidence assessment
Bind each conclusion to the source and locator that can carry it, keeping contrary or missing evidence visible.
Evidence quality and sufficiency remain claim-specific; one source cannot automatically support every adjacent assertion.
Applied scope: Disclosure, meaningful information, and ability to challenge AI outcomes. Documentation, communication of impacts, limits, transparency, and accountability.
Definition and operating context
The canonical concept owner is maha-policy. This route may apply governance; it cannot redefine or inherit the authority of its canonical owner.
This property may publish current-law summaries, policy evidence, Maha proposals labelled as proposals. It must not publish legal advice or proposal presented as enacted law.
Evidence and exact locators
OECD AI Principle 1.3: Transparency and explainability — Principle 1.3. Establishes: Disclosure, meaningful information, and ability to challenge AI outcomes.
Artificial Intelligence Risk Management Framework (AI RMF 1.0) — GOVERN 4.2; MAP 2.2; MEASURE 2.8–2.9. Establishes: Documentation, communication of impacts, limits, transparency, and accountability.
What the evidence does not establish
A principle is not evidence that public trust increased.
AI RMF is voluntary and trustworthiness characteristics can conflict.
This route must not claim legal advice.
This route must not claim proposal presented as enacted law.
Related definitions and applications
same-topic-application: https://policy.mahastrategies.com/policy/public-trust/definition
graphEdges: https://policy.mahastrategies.com/policy/tool-governance/definition
same-topic-application: https://policy.mahastrategies.com/policy/public-trust/uncertainty
same-topic-application: https://policy.mahastrategies.com/policy/public-trust/sources
property-home: https://policy.mahastrategies.com/
same-topic-application: https://policy.mahastrategies.com/policy/public-trust/comparison
same-topic-application: https://policy.mahastrategies.com/policy/public-trust/current-law
Questions this page can answer
What does Public Trust mean in this bounded context?
Public Trust, in this evidence assessment, is limited to the following inspected scope. Disclosure, meaningful information, and ability to challenge AI outcomes. Documentation, communication of impacts, limits, transparency, and accountability. The answer carries the source boundaries forward and does not infer authority from a neighboring topic.
Which inspected sources support this evidence answer?
OECD AI Principle 1.3: Transparency and explainability (principle page inspected 2026-09-05), at Principle 1.3, supports disclosure, meaningful information, and ability to challenge AI outcomes. Artificial Intelligence Risk Management Framework (AI RMF 1.0) (NIST AI 100-1, January 2023), at GOVERN 4.2; MAP 2.2; MEASURE 2.8–2.9, supports documentation, communication of impacts, limits, transparency, and accountability.
What does the evidence not establish?
A principle is not evidence that public trust increased. AI RMF is voluntary and trustworthiness characteristics can conflict. Property boundary: This route may apply governance; it cannot redefine or inherit the authority of its canonical owner.
Which definition or canonical owner must be read first?
This page is the local maha-policy definition for its topic. Related applications may depend on it but may not silently redefine it.
What source, policy, implementation, or release change would require revision?
Re-evaluate this page when a cited source, locator, governing instrument, local implementation, or canonical definition changes. Publication also requires a matching exact-revision review and active canonical release.