The practice of making AI systems’ operation, capabilities, and limitations clear and understandable to relevant stakeholders. Transparency encompasses documentation, explainability, and appropriate disclosure about how AI systems function and are used.

AI transparency enables informed trust and appropriate oversight by helping users, operators, and affected parties understand an AI system’s capabilities, limitations, and decision processes. Different stakeholders require different forms and levels of transparency: technical documentation for developers, intuitive explanations for users, and appropriate disclosures for those affected by AI decisions. Transparency challenges vary by AI type, with some approaches (like rule-based systems) being inherently more interpretable than others (like deep neural networks).

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An organisation implementing transparency practices for their recruitment AI system by documenting which factors the system considers, how they are weighted, providing candidates with explanations for automated decisions, and publishing regular audits of system performance across demographic groups.