Ai Fair Assessment

Learn about AI fairness from our guides and employ cases. Assess and mitigate fairness issues using our Python toolkit. Join our community and contribute metrics, algorithms, and other resources.

Fairness of AI systems is about more than simply running lines of code. In each employ case, both societal and technical aspects shape who might be harmed by AI systems and how. There are various complex sources of unfairness and a variety of societal and technical processes for mitigation, not just the mitigation algorithms in our library.

Throughout this website, you can uncover resources on how to think about fairness as sociotechnical, and how to employ Fairlearn's metrics and algorithms while considering the AI system's broader societal context.

Such details provide a deeper understanding and appreciation for Ai Fair Assessment.