Topics Discussed: Influence from literature and journalism, Are most people good?, Ethical algorithm, Algorithmic fairness of groups vs individuals, Fairness tradeoffs, Facebook, social networks, and algorithmic ethics, Machine learning, Machine learning, Algorithm that determines what is fair, Computer scientists should think about ethics, Algorithmic privacy, Differential privacy, Privacy by misinformation, Privacy of data in society, Game theory, Nash equilibrium, Machine learning and game theory, Mutual assured destruction, Algorithmic trading, Pivotal moment in graduate school.

Michael Kearns Thumbnail

Michael Kearns

Michael Kearns is is an American computer scientist, professor and National Center Chair at the University of Pennsylvania, the founding director of Penn's Singh Program in Networked & Social Systems Engineering (NETS), the founding director of Warren Center for Network and Data Sciences, and also holds secondary appointments in Penn's Wharton School and department of Economics. He is a leading researcher in computational learning theory and algorithmic game theory, and interested in machine learning, artificial intelligence, computational finance, algorithmic trading, computational social science and social networks. He previously led the Advisory and Research function in Morgan Stanley's Artificial Intelligence Center of Excellence team, and is currently an Amazon Scholar within Amazon Web Services.

Books Mentioned in this Podcast with Michael Kearns: