Beyond the Algorithm: Fairness and Machine Learning

from Agents of Intelligence · host Sam Zamany

Join us for a deep dive into the insights of Fairness and Machine Learning by Solon Barocas, Moritz Hardt, and Arvind Narayanan. We’ll explore how fairness is defined and measured, the legal and societal context that shapes it, and the power of causality and counterfactual reasoning in identifying discrimination. From testing bias in practice to understanding “merit and desert,” this episode unpacks the limitations and opportunities of automated decision-making—and why machine learning should never be viewed as a simple replacement for human judgment.

What this episode covers

This episode reveals the tangled web of fairness in machine learning, highlighting how data, law, and social factors intersect to produce (or mitigate) bias. Tune in to learn how thoughtful design and continual oversight can help build more equitable and accountable AI systems.

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Beyond the Algorithm: Fairness and Machine Learning

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This episode is 22 minutes long.

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This episode was published on February 19, 2025.

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