Related papers: Mind the Graph When Balancing Data for Fairness or Robustness

Mind the Graph When Balancing Data for Fairness or Robustness

URL: http://arxiv.org/abs/2406.17433v1
Date: Tue, 25 Jun 2024 10:16:19 GMT
Title: Mind the Graph When Balancing Data for Fairness or Robustness
Authors: Jessica Schrouff, Alexis Bellot, Amal Rannen-Triki, Alan Malek, Isabela Albuquerque, Arthur Gretton, Alexander D'Amour, Silvia Chiappa,
Abstract summary: We define conditions on the training distribution for data balancing to lead to fair or robust models. Our results show that, in many cases, the balanced distribution does not correspond to selectively removing the undesired dependencies. Overall, our results highlight the importance of taking the causal graph into account before performing data balancing.
Score: 73.03155969727038
License: http://creativecommons.org/licenses/by/4.0/
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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