fairlearn

To assess machine learning model fairness for EU AI Act compliance, use this Skill to evaluate AI systems for bias using fairness metrics, implement mitigation algorithms, and generate regulatory documentation aligned with Article 10 requirements.

Bias detection in AI ethics evaluates machine learning models to identify demographic discrimination. This Skill applies fairness metrics to measure disparities across groups, enabling data scientists to pinpoint unfair outcomes in systems like loan application models.

To mitigate fairness risks in a machine learning model, apply the mitigation algorithms provided by this Skill to implement controls that address identified biases, then visualize the results to ensure fairness metrics meet compliance standards.

You can check if your loan application model unfairly discriminates by attaching the model file and using this Skill to run fairness assessments, evaluating predictions against sensitive demographic features to identify and mitigate bias.

The best way to document AI fairness for regulatory assessment is using this Skill to generate compliance documentation, combining fairness metrics, applied mitigation controls, and visualizations to demonstrate alignment with EU AI Act Article 10.