Fairness and Robustness in Invariant Learning: A Case Study in Toxicity Classification

Abstract

Robustness is of central importance in machine learning and has given rise to the fields of domain generalization and invariant learning, which are concerned with improving performance on a test distribution distinct from but related to the training distribution. In light of recent work suggesting an intimate connection between fairness and robustness, we investigate whether algorithms from robust ML can be used to improve the fairness of classifiers that are trained on biased data and tested on unbiased data. We apply Invariant Risk Minimization (IRM), a domain generalization algorithm that employs a causal discovery inspired method to find robust predictors, to the task of fairly predicting the toxicity of internet comments. We show that IRM achieves better out-of-distribution accuracy and fairness than Empirical Risk Minimization (ERM) methods, and analyze both the difficulties that arise when applying IRM in practice and the conditions under which IRM will likely be effective in this scenario. We hope that this work will inspire further studies of how robust machine learning methods relate to algorithmic fairness.

12 pages, 5 figures. Appears in the NeurIPS 2020 Workshop on Algorithmic Fairness through the Lens of Causality and Interpretability

References in corpus (8)

• Language Models are Few-Shot Learners
• Shortcut Learning in Deep Neural Networks
• Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings
• Invariant Risk Minimization
• A Web of Hate: Tackling Hateful Speech in Online Social Spaces
• Assessing Social and Intersectional Biases in Contextualized Word Representations
• An Empirical Study of Invariant Risk Minimization
• Empirical Analysis of Multi-Task Learning for Reducing Model Bias in Toxic Comment Detection

Cited by in corpus (4)

• WILDS: A Benchmark of in-the-Wild Distribution Shifts
• Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond
• Does Robustness Improve Fairness? Approaching Fairness with Word Substitution Robustness Methods for Text Classification
• An Empirical Framework for Domain Generalization in Clinical Settings