In Proceedings of the National Academy of Sciences of the United States of America
The machine learning (ML) research community has landed on automated hate speech detection as the vital tool in the mitigation of bad behavior online. However, it is not clear that this is a widely supported view outside of the ML world. Such a disconnect can have implications for whether automated detection tools are accepted or adopted. Here we lend insight into how other key stakeholders understand the challenge of addressing hate speech and the role automated detection plays in solving it. To do so, we develop and apply a structured approach to dissecting the discourses used by online platform companies, governments, and not-for-profit organizations when discussing hate speech. We find that, where hate speech mitigation is concerned, there is a profound disconnect between the computer science research community and other stakeholder groups-which puts progress on this important problem at serious risk. We identify urgent steps that need to be taken to incorporate computational researchers into a single, coherent, multistakeholder community that is working towards civil discourse online.
Parker Sara, Ruths Derek
2023-Mar-07
hate speech, machine learning, natural language processing
