ABOUT THIS EPISODE
What does it mean for algorithms to be fair or transparent? How does bias arise? How should public policy organize the regulation of AI?
Our sixth episode features Dr. Justin Shin, a postdoctoral Embedded Ethics fellow at Stanford University in partnership with the Institute for Human-Centered AI, as well as the McCoy Family Center for Ethics and Society at Stanford University.
In this episode, Dr. Shin discusses his work on agent causal fairness, highlighting the interdisciplinary nuance and complexity of deciding what is fair. He shares insights from his academic work and thoughts on public policy regarding regulating AI.
Dialexicon is a free podcast dedicated to promoting philosophical education and deep reflection. We invite philosophers from around the globe to discuss philosophy in the context of contemporary injustices—all at an easy-to-understand and digestible level.
Website: https://dialexicon.org
YouTube: https://www.youtube.com/@dialexicon
(00:00) Introduction to the Dialexicon Podcast and Guest
(01:09) Understanding Agent Causal Fairness
(05:00) Criteria for Fairness in Decision Making
(08:59) Overview of Dr. Shin’s Dissertation
(18:49) Exploring Discrimination and Its Legal Implications
(29:52) The Paradox of Punishment and Robots
(30:36) Exploring Punishment Beyond Suffering
(37:10) War Crimes and the Role of Robots
(43:22) AI Development and Public Policy
(50:15) The Role of Government in AI Regulation
(53:17) Philosophy Education and Its Importance
Keywords: AI ethics, algorithmic fairness, discrimination, punishment theory, public policy, philosophy education, transparency, AI regulation
