Case Study: Bias Mitigation for Credit Loan Data

This case study focuses on mitigating gender-based differences in financial lending decisions.

Previously, we discovered that our current model shows a clear bias toward male applicants, resulting in higher false negatives and false positives for female applicants.

In this section, we will explore different strategies to fix this unfairness and see how they affect the performance of our AI model. We will also compare the results with our original model. Get ready to dive into the fascinating world of Responsible AI!