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arXiv · 2205.04238

Counterfactually Augmented Data and Unintended Bias: The Case of Sexism and Hate Speech Detection

Indira Sen·Mattia Samory·Claudia Wagner·Isabelle Augenstein
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Abstract

Counterfactually Augmented Data (CAD) aims to improve out-of-domain generalizability, an indicator of model robustness. The improvement is credited with promoting core features of the construct over spurious artifacts that happen to correlate with it. Yet, over-relying on core features may lead to unintended model bias. Especially, construct-driven CAD -- perturbations of core features -- may induce models to ignore the context in which core features are used. Here, we test models for sexism and hate speech detection on challenging data: non-hateful and non-sexist usage of identity and gendered terms. In these hard cases, models trained on CAD, especially construct-driven CAD, show higher false-positive rates than models trained on the original, unperturbed data. Using a diverse set of CAD -- construct-driven and construct-agnostic -- reduces such unintended bias.

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Indira Sen, Mattia Samory, Claudia Wagner, Isabelle Augenstein. 2022-05-09. Counterfactually Augmented Data and Unintended Bias: The Case of Sexism and Hate Speech Detection. https://arxiv.org/abs/2205.04238

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