Counterfactually Fair Regression with Double Machine Learning
This addresses fairness in AI for scenarios like hiring and education, but it is incremental as it adapts existing causal methods to fairness problems.
The paper tackles counterfactual fairness in regression by proposing Double Machine Learning (DML) Fairness, which uses machine learning to partial out sensitive variable effects, and demonstrates it in a simulation on workplace hiring and a real-world application estimating law school GPAs.
Counterfactual fairness is an approach to AI fairness that tries to make decisions based on the outcomes that an individual with some kind of sensitive status would have had without this status. This paper proposes Double Machine Learning (DML) Fairness which analogises this problem of counterfactual fairness in regression problems to that of estimating counterfactual outcomes in causal inference under the Potential Outcomes framework. It uses arbitrary machine learning methods to partial out the effect of sensitive variables on nonsensitive variables and outcomes. Assuming that the effects of the two sets of variables are additively separable, outcomes will be approximately equalised and individual-level outcomes will be counterfactually fair. This paper demonstrates the approach in a simulation study pertaining to discrimination in workplace hiring and an application on real data estimating the GPAs of law school students. It then discusses when it is appropriate to apply such a method to problems of real-world discrimination where constructs are conceptually complex and finally, whether DML Fairness can achieve justice in these settings.