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Bias-measuring datasets play a critical role in detecting biased behavior oflanguage models and in evaluating progress of bias mitigation methods. In thiswork, we focus on evaluating gender bias through coreference resolution, whereprevious datasets are either hand-crafted or fail to reliably measure anexplicitly defined bias. To overcome these shortcomings, we propose a novelmethod to collect diverse, natural, and minimally distant text pairs viacounterfactual generation, and construct Counter-GAP, an annotated datasetconsisting of 4008 instances grouped into 1002 quadruples. We further identifya bias cancellation problem in previous group-level metrics on Counter-GAP, andpropose to use the difference between inconsistency across genders and withingenders to measure bias at a quadruple level. Our results show that fourpre-trained language models are significantly more inconsistent acrossdifferent gender groups than within each group, and that a name-basedcounterfactual data augmentation method is more effective to mitigate such biasthan an anonymization-based method.

Counter-GAP: Counterfactual Bias Evaluation through Gendered Ambiguous Pronouns