Equalizing Recourse across Groups

By Vivek Gupta, Pegah Nokhiz, Chitradeep Dutta Roy, Suresh Venkatasubramanian. 2019

84 citations recorded by the source corpus.

The rise in machine learning-assisted decision-making has led to concerns about the fairness of the decisions and techniques to mitigate problems of discrimination. If a negative decision is made about an individual (denying a loan, rejecting an application for housing, and so on) justice dictates that we be able to ask how we might change circumstances to get a favorable decision the next time. Moreover, the ability to change circumstances (a better education, improved credentials) should not be limited to only those with access to expensive resources. In other words, \emph{recourse} for negative decisions should be considered a desirable value that can be equalized across (demographically defined) groups. This paper describes how to build models that make accurate predictions while still ensuring that the penalties for a negative outcome do not disadvantage different groups disproportionately. We measure recourse as the distance of an individual from the decision boundary of a classifier. We then introduce a regularized objective to minimize the difference in recourse across groups. We explore linear settings and further extend recourse to non-linear settings as well as model-agnostic settings where the exact distance from boundary cannot be calculated. Our results show that we can successfully decrease the unfairness in recourse while maintaining classifier performance.

Subfields: Artificial Intelligence, Safety Research

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Citations in the corpus

On the Fairness of Causal Algorithmic Recourse (2020, methodology): “The main focus of this paper is the fairness of recourse actions which, to the best of our knowledge, was studied for the first time by Gupta et al. (2019).”
Algorithmic recourse under imperfect causal knowledge: a probabilistic approach (2020, background): “The importance of causal reasoning for an ethical and socially beneficial use of ML-assisted technology has also been stressed in a number of recent works in the field of explainability and fair algorithmic decision making [29, 42, 24, 63, 64, 10, 57, 15].”
A survey of algorithmic recourse: definitions, formulations, solutions, and prospects (2020, background): “, fairness [42, 77, 91, 153, 171], which we explore further below.”
Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review (2020, unknown): “[132] propose adding a regularizer while training a classifier that encourages the classifier to maintain a similar distance of the decision bound-”