Chapter
Machine Learning Researcher Shares Skepticism on Algorithmic Fairness
52:31 - 1:01:01 (08:29)
The speaker is leery of letting algorithms explore alternative definitions of fairness, and believes that social media platforms are currently in a bad equilibrium. They believe that while machine learning has had great successes, these successes have been on a narrow set of tasks.
Clips
The speaker suggests that social media platforms, such as YouTube and Facebook, could experiment with allowing users to see content that is not solely based on their interests and beliefs, in order to break out of the current "bad equilibrium."
52:31 - 57:33 (05:01)
Social Media
Summary
The speaker suggests that social media platforms, such as YouTube and Facebook, could experiment with allowing users to see content that is not solely based on their interests and beliefs, in order to break out of the current "bad equilibrium."
ChapterMachine Learning Researcher Shares Skepticism on Algorithmic Fairness
EpisodeMichael Kearns: Algorithmic Fairness, Bias, Privacy, and Ethics in Machine Learning
PodcastLex Fridman Podcast
The speaker discusses their reservations about using algorithms to explore definitions of fairness, and how the successes in machine learning have been limited to a narrow set of tasks.
57:33 - 1:01:01 (03:28)
Machine Learning
Summary
The speaker discusses their reservations about using algorithms to explore definitions of fairness, and how the successes in machine learning have been limited to a narrow set of tasks.