CF-06: Transparency & Explainability

Back to the AI Control Library

12 controls | 12 core | 0 supplemental

Why this family matters

People need to know when they are dealing with AI, what it can reasonably do, and where its output needs scrutiny. This family addresses the information required for users, operators and affected people to understand an AI system's role and limitations. Transparency must be useful to the audience receiving it.

What this family covers

The controls cover explainability requirements and evaluation, AI identity disclosure, capability and limitation notices, source attribution, uncertainty, oversight transparency and explanation rights. They also address transparency expectations for systems used in higher-impact settings.

How the four control types apply

Contextual controls shape how users interpret and rely on AI through disclosure, explanations and information about uncertainty. Operational controls establish the processes for producing and reviewing that information. Technical mechanisms support delivery and traceability, while Governance controls establish applicable external disclosure obligations.

Where to begin

Identify each audience and the decisions it must make, then provide clear information about AI involvement, limitations and available oversight.

Controls in this family

This family contains 12 controls: 12 core and 0 supplemental. Each page provides requirements, implementation steps, verification criteria, evidence, escalation and a worked example. Applicability depends on the system, information and risks involved.

Core controls

Applying this family

Define the audience and decision before choosing an explanation or disclosure. Use supported facts, tested capability claims and usable contact routes. Distinguish system-level information, explanation of a specific outcome and evidence of human involvement. Revisit communications when the underlying system or operating arrangements change.

Browse all control families

Version 1.0

Scroll to Top