Bias in Machine Learning

This lecture explores the impact of bias in machine learning models, using a game scenario to illustrate the consequences of biased decisions. The instructor discusses real-world cases where biased machine learning systems have led to harmful outcomes, emphasizing the importance of evaluating who could be affected by the models being developed. Strategies such as stakeholder analysis and risk analysis are presented as tools to mitigate potential harm. The lecture also delves into the ethical implications of machine learning applications, highlighting the need to consider fairness and discrimination in decision-making settings.