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This research identifies a critical vulnerability in hate speech detection systems where model performance inadvertently encodes authorship information, thereby compromising user privacy. The authors propose the "privacy-HSD trade-off" framework and benchmark privatization techniques to demonstrate that balancing robust content moderation with individual data protection is feasible but requires careful engineering. This work advances AI safety by highlighting the ethical constraints of deploying large language models for social monitoring without explicit privacy safeguards.
Introducing the Privacy-HSD Trade-off: Hate Speech Detection, but not at the Cost of Privacy
arXiv:2608.19006v1 Announce Type: new Abstract: 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.
Abstract: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.
13 pages, 1 figure, 3 tables. Accepted to WOAH 2026
Computation and Language (cs.CL)
arXiv:2608.19006 [cs.CL]
(or arXiv:2608.19006v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2608.19006 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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