Michael Kearns: Algorithmic Fairness, Bias, Privacy, and Ethics in Machine Learning
Analysis
This article summarizes a podcast episode featuring Michael Kearns, a professor at the University of Pennsylvania, discussing algorithmic fairness, bias, privacy, and ethics in machine learning. The conversation, part of the Artificial Intelligence podcast, delves into Kearns's work, including his book "Ethical Algorithm." The episode covers various aspects of ethical considerations in AI, such as fairness trade-offs and the role of social networks like Facebook. The article also mentions other fields Kearns is involved in, like learning theory, game theory, and computational social science, highlighting the breadth of his expertise. The podcast provides timestamps for different discussion points.
Key Takeaways
- •The podcast episode focuses on ethical considerations in machine learning, particularly algorithmic fairness and bias.
- •Michael Kearns, a leading researcher, discusses his work on ethical algorithms and related topics.
- •The conversation touches upon the impact of social networks and the trade-offs involved in achieving fairness.
“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.”