Rethinking Fair Representation Learning for Performance-Sensitive Tasks
Charles Jones Fabio De Sousa Ribeiro Mélanie Roschewitz Daniel C. Castro Ben Glocker
We investigate the prominent class of fair representation learning methods for bias mitigation. Using causal reasoning to define and formalise different sources of dataset bias, we reveal important implicit assumptions inherent to these methods. We prove fundamental limitations on fair representation learning when evaluation data is drawn from the same distribution as training data and run experiments across a range of medical modalities to examine the performance of fair representation learning under distribution shifts. Our results explain apparent contradictions in the existing literature and reveal how rarely considered causal and statistical aspects of the underlying data affect the validity of fair representation learning. We raise doubts about current evaluation practices and the applicability of fair representation learning methods in performance-sensitive settings. We argue that fine-grained analysis of dataset biases should play a key role in the field moving forward.
Fairness, Causality, Representation Learning, Distribution Shift
We provide theoretical proofs and empirical evidence to reveal fundamental limitations of fair representation learning methods.