Practical requirements extracted from policy instruments, with the source article, the actors they bind, evidence examples and original framework mappings. Legal requirements are marked; everything else is voluntary guidance.
57 results · page 3 of 3
NIST AI RMF · MAP function
Map establishes the context: intended purposes, users, deployment settings, legal requirements, risk categorisation, benefits and costs, and impacts on individuals, groups, communities and society.
UK AI regulation framework · Principle 1, Part 3
Regulators are asked to ensure AI systems function in a robust, secure and safe way, with risks continually identified, assessed and managed. In practice this is enforced through existing safety, security and data-protection law rather than a new duty.
NIST AI RMF · MANAGE function
Manage allocates resources to mapped and measured risks, plans responses including decommissioning, manages third-party risks, and documents post-deployment monitoring, incident response and communication.
NIST AI RMF · MEASURE function
Measure covers selecting metrics and test methods, evaluating validity, safety, security, resilience, explainability, privacy, fairness and bias, and monitoring these over time, including through independent review and red-teaming for generative AI.
UK AI regulation framework · Principle 2, Part 3
Organisations should communicate when and how AI is used and provide explanations proportionate to the risk, so that people can understand decisions affecting them. For personal data, UK GDPR transparency and automated decision-making rights make this binding in practice.
Singapore Model AI Governance Framework · Generative AI framework, dimensions on incident reporting and content provenance
The generative-AI framework recommends incident-reporting channels and processes for AI harms, and content provenance measures such as digital watermarking and cryptographic provenance so that users can identify AI-generated content.
Australian Voluntary AI Safety Standard · Guardrails 7, 8 and 9
Establish processes for people impacted by AI to challenge use or outcomes; be transparent with other organisations across the AI supply chain about data, models and systems; and keep and maintain records to allow third parties to assess compliance.
Filters
