Causal Conceptions of Fairness and their Consequences with Sharad Goel - #586
Published:Aug 8, 2022 16:57
•1 min read
•Practical AI
Analysis
This article summarizes a discussion about Sharad Goel's ICML 2022 Outstanding Paper award-winning work on causal fairness in machine learning. The conversation explores how causality is applied to fairness, examining two main classes of intent within causal fairness and their differences. It also highlights the contrasting approaches to causality in economics/statistics versus computer science/algorithms, and discusses the potential for suboptimal policies when based on causal definitions. The article provides a concise overview of a complex topic, focusing on the implications of causal reasoning in fairness.
Key Takeaways
- •The discussion centers on applying causality to the concept of fairness in machine learning.
- •Two main classes of intent within causal fairness are explored, highlighting their differences.
- •The article contrasts how causality is treated in different fields (economics/statistics vs. computer science/algorithms).
- •The conversation touches upon the potential for suboptimal policies based on causal definitions of fairness.
Reference
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