"Call me sexist, but...": Revisiting Sexism Detection Using Psychological Scales and Adversarial Samples

Samory, Mattia; Sen, Indira; Kohne, Julian; Floeck, Fabian; Wagner, Claudia

doi:10.48550/arxiv.2004.12764

Cited by 2 publications

(1 citation statement)

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“…The latter issue is particularly relevant to hate speech detection since current hate speech datasets vary in data source, sampling strategy and annotation process (Vidgen and Derczynski, 2020;Poletto et al, 2020), and are known to exhibit annotator biases (Waseem, 2016;Waseem et al, 2018;Sap et al, 2019) as well as topic and author biases (Wiegand et al, 2019;Nejadgholi and Kiritchenko, 2020). Correspondingly, models trained on such datasets have been shown to be overly sensitive to lexical features such as group identifiers (Park et al, 2018;Dixon et al, 2018;Kennedy et al, 2020), and to generalise poorly to other datasets (Nejadgholi and Kiritchenko, 2020;Samory et al, 2020). Therefore, held-out performance on current hate speech datasets is an incomplete and potentially misleading measure of model quality.…”

Section: Introductionmentioning

confidence: 99%

HateCheck: Functional Tests for Hate Speech Detection Models

Röttger¹,

Vidgen²,

Nguyen³

et al. 2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

104

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Detecting online hate is a difficult task that even state-of-the-art models struggle with. Typically, hate speech detection models are evaluated by measuring their performance on held-out test data using metrics such as accuracy and F1 score. However, this approach makes it difficult to identify specific model weak points. It also risks overestimating generalisable model performance due to increasingly well-evidenced systematic gaps and biases in hate speech datasets. To enable more targeted diagnostic insights, we introduce HATECHECK, a suite of functional tests for hate speech detection models. We specify 29 model functionalities motivated by a review of previous research and a series of interviews with civil society stakeholders. We craft test cases for each functionality and validate their quality through a structured annotation process. To illustrate HATECHECK's utility, we test near-state-of-the-art transformer models as well as two popular commercial models, revealing critical model weaknesses.

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