Fairness in Algorithmic Decision-Making

This lecture explores the challenges and implications of using algorithms for decision-making processes, focusing on the concept of fairness. It discusses how biases and historical injustices can be inadvertently encoded in data, affecting outcomes. Various fairness notions and approaches are presented, such as individual fairness, group fairness, and equality of opportunity. The lecture also delves into real-world examples where automated systems exhibited discriminatory behavior, highlighting the importance of addressing fairness in algorithmic decision-making.