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AI Governance

Frameworks, regulation, and organisational practice that determine who is accountable for an AI system's behaviour and how that accountability is evidenced.

Published work
1 analysis
Published
Cited sources
2 sources

Topic overview

  1. Scope

    Coverage of AI governance as a practical accountability problem — frameworks, regulation, and the operational controls that make either meaningful.

  2. Published record

    1 analysis and 2 sources currently define this hub.

  3. Current focus

    A working definition of AI governance, how it differs from safety, ethics and compliance, and what separates a governance policy from a governance control.

Read scope and context

Cornerstone analysis

Publication record 01

Key themes in this topic

03 reference points

  1. Governance

    AI governance establishes who is accountable for an AI system’s behaviour.

  2. Accountability

    A governance control has a named subject, trigger and retained record.

  3. Risk Management

    Frameworks organise the work; binding rules define what is not optional.

Scope and context

A working map of the subject.

Governance is where most AI programmes actually fail. Not at the model layer, and rarely at the infrastructure layer — but at the point where nobody can say who approved a system, what it is permitted to do, or what evidence exists that it still behaves as intended.

This topic covers the frameworks organisations adopt, the regulation they are subject to, and the difference between a governance document and a governance control that is actually enforced.

The evidence standard

Evidence is part of the topic map.

This hub currently connects 1 analysis with 2 sources. Dates and source records stay attached to the claims they support.

How claims are sourced

  1. Sourced

    Primary and regulatory records are preferred.

  2. Dated

    Publication and review context stays visible.

  3. Explicit

    Unverified claims are never presented as known facts.