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.
38 results · page 2 of 2
Australian Voluntary AI Safety Standard · Guardrails 1 and 2
Guardrail 1 asks organisations to set up accountability processes including governance, internal capability and a strategy for regulatory compliance; guardrail 2 asks for a risk-management process to identify and mitigate risks across the AI lifecycle.
NIST AI RMF · GOVERN function
Govern covers policies and procedures for AI risk, roles and responsibilities, workforce diversity and training, organisational culture, stakeholder engagement, and third-party risk management. It is the cross-cutting function that supports the other three.
Singapore Model AI Governance Framework · Second edition, Part on internal governance structures and measures
Organisations should adapt existing governance to AI: clear roles and responsibilities, board and senior management oversight, risk-management and internal controls, and staff training.
UK AI regulation framework · Principle 3, Part 3
AI systems should not undermine legal rights, discriminate unfairly or create unfair market outcomes. The Equality Act 2010 and UK GDPR fairness principle make key parts of this binding.
Singapore Model AI Governance Framework · Second edition, Part on human involvement in AI-augmented decision-making
Using a risk-impact matrix (probability and severity of harm), organisations choose human-in-the-loop, human-over-the-loop or human-out-of-the-loop designs and document the rationale.
UK AI regulation framework · Principle 5, Part 3
Affected people should be able to contest harmful AI decisions or outcomes and obtain redress, through existing complaint routes and regulators.
Australian Voluntary AI Safety Standard · Guardrails 4, 5 and 6
Test AI models and systems before deployment and monitor them in operation; enable meaningful human control and intervention; and inform end users about AI-enabled decisions, interactions with AI and AI-generated content.
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.
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.
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