RE-WEIGHTING BASED GROUP FAIRNESS REGULAR-IZATION VIA CLASSWISE ROBUST OPTIMIZATION

Abstract

Many existing group fairness-aware training methods aim to achieve the group fairness by either re-weighting underrepresented groups based on certain rules or using weakly approximated surrogates for the fairness metrics in the objective as regularization terms. Although each of the learning schemes has its own strength in terms of applicability or performance, respectively, it is difficult for any method in the either category to be considered as a gold standard since their successful performances are typically limited to specific cases. To that end, we propose a principled method, dubbed as FairDRO, which unifies the two learning schemes by incorporating a well-justified group fairness metric into the training objective using a classwise distributionally robust optimization (DRO) framework. We then develop an iterative optimization algorithm that minimizes the resulting objective by automatically producing the correct re-weights for each group. Our experiments show that FairDRO is scalable and easily adaptable to diverse applications, and consistently achieves the state-of-the-art performance on several benchmark datasets in terms of the accuracy-fairness trade-off, compared to recent strong baselines.

1. INTRODUCTION

Machine learning algorithms are increasingly used in various decision-making applications that have societal impact; e.g., crime assessment (Julia Angwin & Kirchner, 2016) , credit estimation (Khandani et al., 2010) , facial recognition (Buolamwini & Gebru, 2018; Wang et al., 2019) , automated filtering in social media (Fan et al., 2021) , AI-assisted hiring (Nguyen & Gatica-Perez, 2016) and law enforcement (Garvie, 2016) . A critical issue in such applications is the potential discrepancy of model performance, e.g., accuracy, across different sensitive groups (e.g., race or gender) (Buolamwini & Gebru, 2018), which is easily observed in the models trained with a vanilla empirical risk minimization (ERM) (Valiant, 1984) when the training data has unwanted bias. To address such issues, the fairnessaware learning has recently drawn attention in the AI research community. One of the objectives of fairness-aware learning is to achieve group fairness, which focuses on the statistical parity of the model prediction across sensitive groups. The so-called in-processing methods typically employ additional machinery for achieving the group fairness while training. Depending on the type of machinery used, recent in-processing methods can be divided into two categories (Caton & Haas, 2020) : regularization based methods and re-weighting based methods. Regularization based methods incorporate fairness-promoting terms to their loss functions. They can often achieve good performance by balancing the accuracy and fairness, but be applied only to certain types of model architectures or tasks, such as DNNs (e.g., MFD (Jung et al., 2021) or FairHSIC (Quadrianto et al., 2019) ) or binary classification tasks (e.g., Cov (Baharlouei et al., 2020) ). On the other hand, re-weighting based methods are more flexible and can be applied to a wider range of models and tasks by adopting simpler strategy to give higher weights to samples from underrepresented groups. However, most of them (e.g., LBC (Jiang & Nachum, 2020), RW (Kamiran & Calders, 2012), and FairBatch (Roh et al., 2020) ) lack sound theoretical justifications for enforcing group fairness and may perform poorly on some benchmark datasets.

