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

À propos de cet épisode

<p>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.</p> <p><span style="font-weight: 400;">This conversation is part of the Artificial Intelligence podcast.</span>&#160;If you would like to get more information about this podcast go to <a href="https://lexfridman.com/ai">https://lexfridman.com/ai</a> or connect with @lexfridman on <a href="https://twitter.com/lexfridman">Twitter</a>, <a href="https://www.linkedin.com/in/lexfridman/">LinkedIn</a>, <a href="https://www.facebook.com/lexfridman">Facebook</a>, <a href="https://medium.com/@lexfridman">Medium</a>, or <a href="https://www.youtube.com/lexfridman">YouTube</a> where you can watch the video versions of these conversations. If you enjoy the podcast, please rate it 5 stars on <a href="https://podcasts.apple.com/us/podcast/artificial-intelligence/id1434243584">Apple Podcasts</a> or support it on <a href="https://www.patreon.com/lexfridman">Patreon</a>. This episode is sponsored by <a href="https://pessimists.co/">Pessimists Archive</a> podcast. Here&#8217;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):</p> <p>00:00 &#8211; Introduction<br /> 02:45 &#8211; Influence from literature and journalism<br /> 07:39 &#8211; Are most people good?<br /> 13:05 &#8211; Ethical algorithm<br /> 24:28 &#8211; Algorithmic fairness of groups vs individuals<br /> 33:36 &#8211; Fairness tradeoffs<br /> 46:29 &#8211; Facebook, social networks, and algorithmic ethics<br /> 58:04 &#8211; Machine learning<br /> 58:05 &#8211; Machine learning<br /> 59:19 &#8211; Algorithm that determines what is fair<br /> 1:01:25 &#8211; Computer scientists should think about ethics<br /> 1:05:59 &#8211; Algorithmic privacy<br /> 1:11:50 &#8211; Differential privacy<br /> 1:19:10 &#8211; Privacy by misinformation<br /> 1:22:31 &#8211; Privacy of data in society<br /> 1:27:49 &#8211; Game theory<br /> 1:29:40 &#8211; Nash equilibrium<br /> 1:30:35 &#8211; Machine learning and game theory<br /> 1:34:52 &#8211; Mutual assured destruction<br /> 1:36:56 &#8211; Algorithmic trading<br /> 1:44:09 &#8211; Pivotal moment in graduate school</p>