Algorithmic Transparency

Algorithmic transparency refers to the capacity to understand, explain, and scrutinize how AI systems reach conclusions or recommendations; it requires disclosure of AI use and explanation of AI-assisted outputs to affected parties to enable oversight, challenge, and public accountability.

Obligations for developers and deployers to provide clear documentation, explanations, and transparency statements about model functionality, decision logic, and potential impacts whenever decisions affect individuals; includes requirements for algorithmic impact assessments and disclosures to enable understanding and challenge by affected persons.

The degree to which an AI system's operating logic, decision-making processes, and influencing factors can be understood and explained to affected individuals and overseers, forming the basis for mandatory disclosure, explanation, and documentation requirements particularly for high‑risk systems.

Algorithmic transparency requires recording details about AI models and algorithms, including their design, parameters, and modifications. For complex systems, it means capturing explanations of how decisions are reached, ensuring understandability and explainability.

The principle that the logic and data used by an AI system should be understandable and accessible to relevant stakeholders.

This involves the obligation of developers and operators to provide clear information about the logic, data, and intended outcomes of AI systems.