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Necessity and Sufficiency for Explaining Text Classifiers: A Case Study in Hate Speech Detection

6 May 2022NAACL 2022 7arXiv:2205.03302archive 2025-07-28

Esma Balkir, Isar Nejadgholi, Kathleen C. Fraser, Svetlana Kiritchenko

We present a novel feature attribution method for explaining text classifiers, and analyze it in the context of hate speech detection. Although feature attribution models usually provide a single importance score for each token, we instead provide two complementary and theoretically-grounded scores -- necessity and sufficiency -- resulting in more informative explanations. We propose a transparent method that calculates these values by generating explicit perturbations of the input text, allowing the importance scores themselves to be explainable. We employ our method to explain the predictions of different hate speech detection models on the same set of curated examples from a test suite, and show that different values of necessity and sufficiency for identity terms correspond to different kinds of false positive errors, exposing sources of classifier bias against marginalized groups.

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1ran · honoured contract
2ran · our draft was wrong
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calculate_necc_and_suff esmab/necessity-sufficiency/perturbation_functions.py official repository unverified no licence file found · pointer only · 9ede2b7ae5f368f2 · report
generate_masked_text esmab/necessity-sufficiency/perturbation_functions.py official repository unverified no licence file found · pointer only · 3ea7391ca2929d12 · report
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Hate Speech Detection

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