Bias and Fairness in Machine Learning

This lecture delves into the critical aspects of bias and fairness in machine learning, exploring the impact of biased data on model predictions and the consequences of unfair algorithms. It discusses the various types of biases, from data generation to model deployment, and highlights the ethical implications of machine learning systems on individuals and society. Through real-world examples and case studies, the instructor emphasizes the importance of responsible design decisions and the need to consider the potential harm that ML systems can cause. The lecture also covers fairness criteria in classification, addressing issues of discrimination, disparities, and the challenges of achieving fairness in algorithmic decision-making.