隐私-仇恨言论检测的权衡：仇恨言论检测，但不以牺牲隐私为代价
Introducing the Privacy-HSD Trade-off: Hate Speech Detection, but not at the Cost of Privacy
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中文总结 AI 辅助
该研究针对仇恨言论检测（HSD）可能牺牲隐私的问题，提出隐私-HSD权衡概念，测试了多种文本私有化方法及新的AgnoSpeech技术，证明平衡两者虽难但可行，呼吁开展更多相关研究。
中文摘要 AI 辅助
仇恨言论是切实且紧迫的威胁，影响着大量网络用户，尤其是青少年和少数群体。尽管构建可靠、鲁棒的自动仇恨言论检测（HSD）系统至关重要，但我们认为这必须与个人隐私权相平衡。通过探索HSD与隐私的交叉领域，我们发现HSD系统可能会在无意中实现性能的同时编码作者身份，从而对隐私构成威胁。基于这些发现，我们确立了隐私-HSD权衡的概念，要求谨慎平衡两者。我们对一系列文本私有化方法以及我们新提出的特定领域AgnoSpeech技术进行了基准测试，结果表明平衡隐私与HSD虽困难但可行。这些发现有力地论证了应开展更多关于隐私与HSD之间权衡的研究，二者对保障网络参与均具有切实意义。
英文摘要
Hate speech is a real and timely threat that affects a large portion of online users, especially youth and minority groups. While building reliable and robust automatic hate speech detection (HSD) systems is paramount, we argue that this must also be balanced with the individual right to privacy. Exploring the intersection of HSD and privacy, we demonstrate that HSD systems might unintentionally achieve performance at the cost of encoding authorship, posing a threat to privacy. Building on these findings, we establish the notion of a privacy-HSD trade-off, which demands a careful balance. We benchmark a series of text privatization methods, as well as our newly proposed domain-specific AgnoSpeech technique, showing that balancing privacy and HSD is difficult but feasible. The findings make a strong case for more research on the trade-offs between privacy and HSD, both of which have tangible implications for the safeguarding of online participation.
发表机构
- Technical University of Munich（慕尼黑工业大学）
- Munich Center for Machine Learning（慕尼黑机器学习中心）
- Munich Data Science Institute（慕尼黑数据科学研究所）
- University of Southern Denmark（南丹麦大学）
- Technical University of Moldova（摩尔多瓦技术大学）
- ICube, University of Strasbourg（斯特拉斯堡大学ICube研究所）
机构由 AI 辅助整理，请以论文原文为准。
