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

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

Lex Fridman Podcast vor 6 Jahren

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1:49:01

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