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[D] Michael Kearns: Algorithmic Fairness, Bias, Privacy, and Ethics in Machine Learning | Artificial Intelligence Podcast

[D] Michael Kearns: Algorithmic Fairness, Bias, Privacy, and Ethics in Machine Learning | Artificial Intelligence Podcast

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.

Video: https://www.youtube.com/watch?v=AzdxbzHtjgs
Audio: https://lexfridman.com/michael-kearns

Outline:
(click on the timestamp to jump to that part of the video)

0:00 – Introduction
2:45 – Influence from literature and journalism
7:39 – 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
58:05 – 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

https://preview.redd.it/auuvygjlooz31.png?width=1280&format=png&auto=webp&s=5001b4f3493cb4aae67caa484fe32b4db0bde477

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