Rethinking Fair Representation Learning for Performance-Sensitive Tasks

2024-10-05

arXiv.org

AI Summary

This work challenges the applicability of fair representation learning in performance-critical domains such as medical diagnosis, exposing its implicit assumption of train–test distributional alignment and demonstrating that fairness in representations does not guarantee downstream task performance even under this assumption. Method: Leveraging causal inference to model bias sources and integrating statistical analysis with multimodal clinical data (imaging, text, time-series), we develop a distribution-shift evaluation framework for out-of-distribution generalization. Contribution/Results: We provide the first systematic theoretical characterization of fair representation learning’s limits from both causal and statistical perspectives. Empirical evaluation across diverse medical tasks reveals significant performance degradation under distribution shift, resolving longstanding contradictions in prior literature. Our findings critically question prevailing black-box fairness optimization paradigms, advocating instead for fine-grained bias analysis grounded in data provenance. The study delivers both theoretical caution and practical guidance for deploying fair representations in high-stakes settings.

Technology Category

• Machine Learning: Ethics, Bias, and Fairness
• Computer Vision: Bias, Fairness & Privacy
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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.

Problem

• fair representation learning bias mitigation
• causal reasoning dataset bias
• evaluation practices performance-sensitive settings

Innovation

• Causal reasoning for bias analysis
• Experiments across medical modalities
• Fine-grained dataset bias analysis