Regulating Algorithmic Bias in Credit Scoring

This cutting-edge course addresses the critical challenge of ensuring fairness and equal access in credit markets driven by **Algorithmic Decision-Making (ADM)** and Machine Learning. It provides a deep technical and legal understanding of how biases—often hidden in training data—can lead to discriminatory outcomes (**Disparate Impact**) against protected classes. Participants will learn methods for **bias detection and mitigation** (e.g., explainable AI/XAI), regulatory compliance with Fair Lending laws in an automated context, and policy approaches for auditability and transparency in credit scoring models, promoting ethical and inclusive AI in finance.