Schrouff, Jessica; Bellot, Alexis; Rannen-Triki, Amal; Malek, Alan; Albuquerque, Isabela; Gretton, Arthur; D'Amour, Alexander; Schrouff, Jessica; Bellot, Alexis; Rannen-Triki, Amal; Malek, Alan; Albuquerque, Isabela; Gretton, Arthur; D'Amour, Alexander; Chiappa, Silvia; - view fewer (2024) Mind the Graph When Balancing Data for Fairness or Robustness. In: Globerson, A and Mackey, L and Belgrave, D and Fan, A and Paquet, U and Tomczak, J and Zhang, C, (eds.) Advances in Neural Information Processing Systems 37 (NeurIPS 2024). (pp. pp. 1-35). NeurIPS: Vancouver, BC, Canada.

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Abstract

Failures of fairness or robustness in machine learning predictive settings can be due to undesired dependencies between covariates, outcomes and auxiliary factors of variation. A common strategy to mitigate these failures is data balancing, which attempts to remove those undesired dependencies. In this work, we define conditions on the training distribution for data balancing to lead to fair or robust models. Our results display that in many cases, the balanced distribution does not correspond to selectively removing the undesired dependencies in a causal graph of the task, leading to multiple failure modes and even interference with other mitigation techniques such as regularization. Overall, our results highlight the importance of taking the causal graph into account before performing data balancing.

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