Unpacking Algorithmic Fairness

This chapter explores the intersection of human decision-making biases and algorithmic biases, advocating for insights from the former to inform the latter's fairness. The conversation critiques traditional mathematical definitions of fairness in algorithms, particularly regarding their inadequacy in high-stakes scenarios like healthcare and justice. It examines the ethical implications of including or excluding protected characteristics such as race and gender in algorithmic evaluations, emphasizing the importance of calibration in achieving true equity.