Michael Kearns: Algorithmic Fairness, Bias, Privacy, and Ethics in Machine Learning

Michael Kearns is a professor at University of Pennsylvania and a
co-author of the new book Ethical Algorithm that is the focus of
much of our conversation, including algorithmic fairness, bias,
privacy, and ethics in general. But, that is just one of many
fields that Michael is a world-class researcher in, some of which
we touch on quickly including learning theory or theoretical
foundations of machine learning, game theory, algorithmic
trading, quantitative finance, computational social science, and
more.


This conversation is part of the Artificial Intelligence
podcast. If you would like to get more information
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on Apple Podcasts or support it on Patreon. This episode is
sponsored by Pessimists Archive podcast. Here’s the outline with
timestamps for this episode (on some players you can click on the
timestamp to jump to that point in the episode):


00:00 – Introduction
02:45 – Influence from literature and journalism
07:39 – Are most people good?
13:05 – Ethical algorithm
24:28 – Algorithmic fairness of groups vs individuals
33:36 – Fairness tradeoffs
46:29 – Facebook, social networks, and algorithmic ethics
58:04 – Machine learning
58:05 – Machine learning
59:19 – Algorithm that determines what is fair
1:01:25 – Computer scientists should think about ethics
1:05:59 – Algorithmic privacy
1:11:50 – Differential privacy
1:19:10 – Privacy by misinformation
1:22:31 – Privacy of data in society
1:27:49 – Game theory
1:29:40 – Nash equilibrium
1:30:35 – Machine learning and game theory
1:34:52 – Mutual assured destruction
1:36:56 – Algorithmic trading
1:44:09 – Pivotal moment in graduate school