Counterfactual Fairness
Matt J. Kusner, Joshua R. Loftus, Chris Russell, Ricardo Silva

TL;DR
This paper introduces a causal inference-based framework for counterfactual fairness, ensuring machine learning decisions are unbiased across demographic groups, demonstrated through a law school success prediction case.
Contribution
It proposes a novel formalization of fairness using counterfactual reasoning, addressing biases in historical data for fairer machine learning models.
Findings
Framework effectively models fairness using causal inference.
Application to law school success prediction shows improved fairness.
Demonstrates the importance of counterfactual analysis in ethical AI.
Abstract
Machine learning can impact people with legal or ethical consequences when it is used to automate decisions in areas such as insurance, lending, hiring, and predictive policing. In many of these scenarios, previous decisions have been made that are unfairly biased against certain subpopulations, for example those of a particular race, gender, or sexual orientation. Since this past data may be biased, machine learning predictors must account for this to avoid perpetuating or creating discriminatory practices. In this paper, we develop a framework for modeling fairness using tools from causal inference. Our definition of counterfactual fairness captures the intuition that a decision is fair towards an individual if it is the same in (a) the actual world and (b) a counterfactual world where the individual belonged to a different demographic group. We demonstrate our framework on a…
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Code & Models
Videos
Counterfactual Fairness· youtube
Taxonomy
TopicsEthics and Social Impacts of AI · Law, Economics, and Judicial Systems · Privacy-Preserving Technologies in Data
