超越谴责仇恨：应对语言中隐含偏见与刻板印象的策略
Beyond Denouncing Hate: Strategies for Countering Implied Biases and Stereotypes in Language
AI总结：
本文借鉴心理学与哲学提出六种反驳隐含刻板印象的策略，通过用户研究和数据集对比发现人类反言论更具针对性且更有说服力，而机器生成反言论策略较笼统，揭示了在反言论生成中考虑潜在刻板印象含义的重要性。
摘要：
反言论，即用于抵消仇恨言论潜在危害的回应，已成为在不进行审查的情况下解决在线仇恨言论的日益流行的方案。然而，恰当地应对仇恨语言需要反驳并消除此类语言所隐含的潜在不准确刻板印象。在这项工作中，我们借鉴心理学和哲学文献，设计了六种受心理学启发的策略，以挑战仇恨语言中潜在的刻板印象含义。我们首先通过一项用户研究考察每种策略的说服力，然后比较它们在人类生成和机器生成的反言论数据集中的使用情况。我们的结果表明，人类撰写的反言论使用了更具体针对隐含刻板印象的反驳策略（例如，针对刻板印象的反例、关于刻板印象起源的外部因素），而机器生成的反言论则使用了较不具体的策略（例如，笼统地谴责言论的仇恨性）。此外，与人类生成的反言论相比，机器生成的反言论往往采用人类认为说服力较弱的策略。我们的发现表明，在生成反言论时考虑言论中潜在的刻板印象含义以及改进机器对反刻板印象示例的推理具有重要意义。
英文摘要：
Counterspeech, i.e., responses to counteract potential harms of hateful speech, has become an increasingly popular solution to address online hate speech without censorship. However, properly countering hateful language requires countering and dispelling the underlying inaccurate stereotypes implied by such language. In this work, we draw from psychology and philosophy literature to craft six psychologically inspired strategies to challenge the underlying stereotypical implications of hateful language. We first examine the convincingness of each of these strategies through a user study, and then compare their usages in both human- and machine-generated counterspeech datasets. Our results show that human-written counterspeech uses countering strategies that are more specific to the implied stereotype (e.g., counter examples to the stereotype, external factors about the stereotype's origins), whereas machine-generated counterspeech uses less specific strategies (e.g., generally denouncing the hatefulness of speech). Furthermore, machine-generated counterspeech often employs strategies that humans deem less convincing compared to human-produced counterspeech. Our findings point to the importance of accounting for the underlying stereotypical implications of speech when generating counterspeech and for better machine reasoning about anti-stereotypical examples.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
