Ethics and Fairness in Machine Learning

This lecture delves into the ethical implications of deploying machine learning algorithms in real-world applications, emphasizing the importance of considering fairness. The instructor highlights how algorithms can perpetuate discrimination in decision-making processes, especially in domains like education, employment, and healthcare. The lecture stresses the need for caution and awareness among future software engineers and data scientists, as the decisions made by algorithms may not inherently be fair. Through examples like Amazon's same-day delivery coverage and the impact of demographic disparities in machine learning pipelines, the lecture prompts critical thinking about the ethical implications of algorithmic decision-making. It also explores different fairness criteria in statistical classification, such as independence, separation, and sufficiency, to assess and address biases in machine learning models.