Lex Fridman Podcast Michael Kearns: Algorithmic Fairness, Bias, Privacy, and Ethics in Machine Learning
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Nov 19, 2019 Michael Kearns, a professor at the University of Pennsylvania and co-author of 'Ethical Algorithms,' dives into the fascinating world of algorithmic fairness and bias. He discusses the interplay between ethics and technology, and how social norms influence perceptions of fairness. Kearns explores the ethical dilemmas of engaging users versus ensuring fairness in algorithms, the role of differential privacy in safeguarding data, and the dynamic relationship between game theory and machine learning. A thought-provoking conversation on balancing human values with technological advancement!
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English Major to Computer Science
- Michael Kearns initially pursued an English major, influenced by literature and journalism, before switching to math and computer science.
- He realized that an English major teaches you how to read but not write, then transitioned to technical fields.
Algorithmic Ethics: Low-Hanging Fruit and Deeper Questions
- Algorithmic solutions address blatant unfairness and privacy violations in algorithms.
- Deeper ethical considerations arise when dealing with nuanced situations, requiring philosophical inquiry.
The Goodness of People and Social Norms
- Michael Kearns believes most people are inherently good, and negative behaviors often stem from circumstances, not character.
- Social norms within groups can lead to behaviors that seem unusual to outsiders, potentially causing abuses of power.
