Many recent mainstream media articles and popular books have raised alarms over anti-social algorithmic behavior, especially regarding machine learning and artificial intelligence. The concerns include leaks of sensitive personal data by predictive models, algorithmic discrimination as a side-effect of machine learning, and inscrutable decisions made by complex models. While standard and legitimate responses to these phenomena include calls for stronger and better laws and regulations, researchers in machine learning, statistics and related areas are also working on designing better-behaved algorithms. An explosion of recent research in areas such as differential privacy, algorithmic fairness and algorithmic game theory is forging a new science of socially aware algorithm design. I will survey these developments and attempt to place them in a broader societal context. This episode is based on the book “The Ethical Algorithm”, co-authored with Aaron Roth (Oxford University Press, 2019).

Michael Kearns is Professor and National Center Chair, Department of Computer and Information Science at the University of Pennsylvania