Algorithmic Fairness From The Lens Of Causality And Information Theory
Offered By: Simons Institute via YouTube
Course Description
Overview
Syllabus
Intro
Motivation: Machine Learning in High-Stakes Applications
How to identify/explain sources of disparity in machine learning models?
Outline
Popular Definition: Statistical Parity
Conditional Dependence can sometimes falsely detect bias (misleading dependencies) even when a model is "causally" fair Example: Causally fair model
One causal measure that satisfies all desirable properties Theorem: Our proposed measure of non-exempt disparity, given by
Some intuition on our proposed measure from causality
Non-negative decomposition of total "causal" disparity Theorem 2 (pictorially illustrated)
Simulation: Four types of disparities present
Numerical Computation of Fundamental Limits on the Tradeoff 1.4
Reliable Machine Learning
Partial Information Decomposition + Causality
Taught by
Simons Institute
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