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Responsible AI resources

Governance & accountability

Follow the decisions, ownership, and oversight behind a system.

Practical resources

Controls, methods & references

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Full report: Enterprise Assurance Stack: What’s New

Published analysis

Briefings and research updates

4 published briefings in this topic. Coverage reflects this collection, not the size or importance of the field.

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Subject overview and introductory questions

Governance is how people decide whether, where, and how an AI system should be used. Accountability connects those decisions to named responsibilities, reviewable evidence, and a way to challenge or correct an outcome.

Three questions worth asking.

A written policy describes an intention. Evidence of implementation is needed to assess what happens in practice.

A visual introduction

Follow the responsibility

Trace a decision from the person who owns it to the person who may need it corrected.

Explore: Ownership

Responsibility needs to attach to people with authority to approve, change, or stop the use of a system.

Follow the evidenceLook for named responsibilities and decision rights.

Read the complete explanation

Ownership

Responsibility needs to attach to people with authority to approve, change, or stop the use of a system.

Look for named responsibilities and decision rights.

Challenge

Review should be able to question the assumptions, evidence, and consequences of a decision, with enough independence to matter.

Look for documented reasoning, meaningful challenge, and a response to findings.

Remedy

Accountability includes a route to question an outcome and seek correction when something has gone wrong.

Look for an accessible appeal or correction process and evidence that it works.

Conceptual explainerSeptember 7, 2026

Conceptual responsibilities. Organizational structures vary; a policy document alone does not demonstrate effective accountability.
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