Addressing Both Statistical and Causal Gender Fairness in NLP Models (2024)

Abstract: Statistical fairness stipulates equivalent outcomes for every protected group, whereas causal fairness prescribes that a model makes the same prediction for an individual regardless of their protected characteristics. Counterfactual data augmentation (CDA) is effective for reducing bias in NLP models, yet models trained with CDA are often evaluated only on metrics that are closely tied to the causal fairness notion; similarly, sampling-based methods designed to promote statistical fairness are rarely evaluated for causal fairness. In this work, we evaluate both statistical and causal debiasing methods for gender bias in NLP models, and find that while such methods are effective at reducing bias as measured by the targeted metric, they do not necessarily improve results on other bias metrics. We demonstrate that combinations of statistical and causal debiasing techniques are able to reduce bias measured through both types of metrics.

## introduction auditing nlp models is crucial to measure potential biases that can lead to unfair or discriminatory outcomes when models are deployed. several methods have been proposed to quantify social biases in nlp models including intrinsic metrics that probe bias in the internal representations of the model @xcite @xcite and extrinsic metrics that measure model behavioral differences across protected groups (e.g., gender and race). in this paper, we focus on extrinsic metrics as they align directly with how models are used in downstream tasks @xcite . proposed extrinsic bias metrics can be categorized based on whether they correspond to a statistical or causal notion of fairness. a bias metric quantifies model bias based on a fairness criterion. two common kinds of fairness criteria are statistical and causal fairness. statistical fairness calls for statistically equivalent outcomes for all protected groups. statistical bias metrics estimate the difference in prediction outcomes between protected groups based on observational data @xcite . causal fairness shifts the focus from statistical association to identifying root causes of unfairness through causal reasoning @xcite . causal bias metrics measure the effect of the protected attribute on the model's predictions via interventions that change the value of the protected attribute. a model satisfies counterfactual fairness, as defined by @xcite , if the same prediction is made for an individual in both the actual world and in the counterfactual world in which the protected attribute is changed. while there is no consensus on which metric is the right one to use @xcite , most work on bias mitigation only uses a single type of metric in their evaluation. this is typically a metric that is closely connected to the proposed debiasing method. for example, counterfactual data augmentation (cda) @xcite , has been shown to reduce bias in nlp models. however, prior works that adopt this method often evaluate only on causal bias metrics and do not include any tests using statistical bias metrics @xcite @xcite @xcite . we find only one exception- @xcite found causal debiasing exhibits some tradeoffs between statistical and causal metrics (section 2.3). this raises concerns about the effectiveness and reliability of these debiasing methods in settings where multiple fairness criteria may be desired. in this work, we first show that methods designed to reduce bias according to one fairness criteria often do not reduce bias as measured by other bias metrics. then, we propose training methods to achieve statistical and causal fairness for gender in nlp models. we focus on gender bias as it is a well-studied problem in the literature. ## background this section provides background on bias metrics based on statistical and causal notions of fairness and overviews bias mitigation techniques. ## bias metrics are disparate disparities between different statistical fairness definitions and group and individual fairness have been studied in the tabular data settings (section 2.3). we focus on the most common type of bias metrics, statistical and causal, used for evaluating nlp tasks. we first explain why statistical and causal bias metrics may produce inconsistent results. we then report on the experiments to measure disparities between the metrics on evaluating gender bias in an occupation classification task. ## cross-evaluation this section cross-evaluates the effectiveness of existing debiasing methods on gender bias in an occupation classification and toxicity detection task. we show using statistical and causal debiasing methods alone may not achieve both types of fairness. ## achieving both statistical and causal fairness in the previous section, we saw that using either statistical or causal debiasing method alone may not achieve both statistical and causal fairness. to counter this problem, this section considers simple methods that combine both statistical and causal debiasing techniques. ## summary we demonstrate the disparities between statistical and causal bias metrics and provide insight into how and why optimizing based on one type of metric does not necessarily improve the other. we show this by cross-evaluating existing statistical and causal debiasing methods on both metrics and find that they sometimes may even worsen the other type of bias metrics. to obtain models that perform well on both types of bias metrics, we introduce simple debiasing strategies that combine both statistical and causal debiasing techniques.