Fair Hate Speech Detection through Evaluation of Social Group Counterfactuals

• URL: http://arxiv.org/abs/2010.12779v1
• Date: Sat, 24 Oct 2020 04:51:47 GMT
• Title: Fair Hate Speech Detection through Evaluation of Social Group Counterfactuals
• Authors: Aida Mostafazadeh Davani, Ali Omrani, Brendan Kennedy, Mohammad Atari, Xiang Ren, Morteza Dehghani
• Abstract summary: Approaches for mitigating bias in supervised models are designed to reduce models' dependence on specific sensitive features of the input data. In the case of hate speech detection, it is not always desirable to equalize the effects of social groups. Counterfactual token fairness for a mentioned social group evaluates the model's predictions as to whether they are the same for (a) the actual sentence and (b) a counterfactual instance. Our approach assures robust model predictions for counterfactuals that imply similar meaning as the actual sentence.
• Score: 21.375422346539004
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Approaches for mitigating bias in supervised models are designed to reduce models' dependence on specific sensitive features of the input data, e.g., mentioned social groups. However, in the case of hate speech detection, it is not always desirable to equalize the effects of social groups because of their essential role in distinguishing outgroup-derogatory hate, such that particular types of hateful rhetoric carry the intended meaning only when contextualized around certain social group tokens. Counterfactual token fairness for a mentioned social group evaluates the model's predictions as to whether they are the same for (a) the actual sentence and (b) a counterfactual instance, which is generated by changing the mentioned social group in the sentence. Our approach assures robust model predictions for counterfactuals that imply similar meaning as the actual sentence. To quantify the similarity of a sentence and its counterfactual, we compare their likelihood score calculated by generative language models. By equalizing model behaviors on each sentence and its counterfactuals, we mitigate bias in the proposed model while preserving the overall classification performance.

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