2 papers
cs.LG2024
Evaluating Model Bias Requires Characterizing its Mistakes
Isabela Albuquerque, Jessica Schrouff, David Warde-Farley +3
The ability to properly benchmark model performance in the face of spurious correlations is important to both build better predictors and increase confidence that models are operat…
cs.LG2024
Mind the Graph When Balancing Data for Fairness or Robustness
Jessica Schrouff, Alexis Bellot, Amal Rannen-Triki +5
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 c…