Counterfactual Fairness

by   Matt J. Kusner, et al.
The Alan Turing Institute
NYU college

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 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 real-world problem of fair prediction of success in law school.


On Learning and Testing of Counterfactual Fairness through Data Preprocessing

Machine learning has become more important in real-life decision-making ...

Equal Opportunity and Affirmative Action via Counterfactual Predictions

Machine learning (ML) can automate decision-making by learning to predic...

Counterfactual harm

To act safely and ethically in the real world, agents must be able to re...

Counterfactually Fair Regression with Double Machine Learning

Counterfactual fairness is an approach to AI fairness that tries to make...

Group Fairness by Probabilistic Modeling with Latent Fair Decisions

Machine learning systems are increasingly being used to make impactful d...

Pretending Fair Decisions via Stealthily Biased Sampling

Fairness by decision-makers is believed to be auditable by third parties...

Counterfactual Fairness in Mortgage Lending via Matching and Randomization

Unfairness in mortgage lending has created generational inequality among...

Please sign up or login with your details

Forgot password? Click here to reset