Detecting & Mitigating Bias

This lesson focuses on practical methods used to identify and reduce bias in AI systems. Learners examine subgroup analysis, fairness metrics, error rate comparisons, and model auditing techniques used to uncover hidden disparities. The lesson also explores mitigation strategies such as relabeling, reweighing, and dataset diversification, helping students understand how organizations build more equitable, trustworthy, and responsible AI solutions.