Module 3: How Do We Measure Fairness AI скачать в хорошем качестве

Module 3: How Do We Measure Fairness AI

Measuring Algorithmic Bias and Fairness Metrics This video outlines a comprehensive framework for identifying and mitigating algorithmic bias within automated decision systems. It defines key group fairness metrics—such as demographic parity and equalised odds—while contrasting them with individual fairness through the use of counterfactual scenarios. To move from theory to practice, the material introduces statistical tools and software like Fairlearn and IBM AIF360, which allow developers to measure disparities and manage the inevitable trade-offs between accuracy and equity. The sources also address complex obstacles, including data scarcity and intersectionality, which can obscure how bias affects individuals with overlapping identities. Finally, a public housing case study illustrates the real-world necessity of transparent audits and community engagement to ensure technology does not reinforce historical inequalities. Overall, the text serves as a technical and ethical guide for building accountable, measurable, and responsible artificial intelligence.