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 about this
podcast go to https://lexfridman.com/ai or connect with @lexfridman
on Twitter, LinkedIn, Facebook, Medium, or YouTube where you can
watch the video versions of these conversations. If you enjoy the
podcast, please rate it 5 stars 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