AI transparency: what it means and what regulators expect

What AI transparency covers

Transparency in AI is not one thing. It includes telling people when they are interacting with an AI system, disclosing what data the system was trained on, explaining how decisions are reached, documenting known limitations and failure modes, and publishing performance metrics.

Different frameworks emphasise different parts. The EU AI Act focuses on informing users and affected people. ISO 42001 focuses on documented information within the management system. The NIST AI RMF treats transparency as a cross-cutting property of trustworthy AI.

Transparency under the EU AI Act

The EU AI Act sets transparency obligations that scale with risk. All AI systems that interact with people must disclose that the user is dealing with AI, unless it is obvious. Systems that generate synthetic content must label it. High-risk systems must come with technical documentation, instructions for use and logging.

• Disclosure that a system is AI-powered
• Labelling of deepfakes and synthetic content
• Technical documentation for high-risk systems
• Instructions for use, including limitations
• Logging so that decisions can be traced

Making it part of the programme

In Certifyi, transparency requirements map to controls. The control for disclosure links to the same framework mapping as your other controls, with evidence attached. When the EU AI Act's transparency rules and ISO 42001's documentation requirements overlap, the evidence is shared.

• AI compliance: ISO 42001, EU AI Act and NIST AI RMF in one workspace.
• AI risk management: register, classify and govern AI systems.
• AI impact assessment: assessing who is affected and how.

Related terms

AI impact assessment

An assessment, for each AI system, of who is affected, what could go wrong, how likely and how severe it is, and what is done about it. ISO 42001 requires one, and the EU AI Act requires its own forms for high-risk systems.

Algorithmic bias

A systematic and unfair difference in outcomes produced by an AI system, caused by training data, feature selection, or design choices that disadvantage one group over another.

Human oversight

The requirement that a qualified person can monitor, intervene in, or override an AI system's decisions. Required by the EU AI Act for high-risk AI systems and by ISO 42001 as a control objective.