Suum Cuique: Studying Bias in Taboo Detection with a Community Perspective

Findings of the Association for Computational Linguistics: ACL 2022, pages 2883 - 2896 May 22-27, 2022 c⃝2022 Association for Computational Linguistics Suum Cuique: Studying Bias in Taboo Detection with a Community Perspective⋆ Osama Khalid†, Jonathan Rusert†, Padmini Srinivasan University of Iowa {osama-khalid, jonathan-rusert, padmini-srinivasan}@uiowa.edu Abstract Prior research has discussed and illustrated the need to consider linguistic norms at the community level when studying taboo (hate- ful/offensive/toxic etc.) language. However, a methodology for doing so, that is firmly founded on community language norms is still largely absent. This can lead both to biases in taboo text classification and limitations in our understanding of the causes of bias. We propose a method to study bias in taboo clas- sification and annotation where a community perspective is front and center. This is accom- plished by using special classifiers tuned for each community’s language. In essence, these classifiers represent community level language norms. We use these to study bias and find, for example, biases are largest against African Americans (7/10 datasets and all 3 classifiers examined). In contrast to previous papers we also study other communities and find, for ex- ample, strong biases against South Asians. In a small scale user study we illustrate our key idea which is that common utterances, i.e., those with high alignment scores with a community (community classifier confidence scores) are unlikely to be regarded taboo. Annotators who are community members contradict taboo clas- sification decisions and annotations in a major- ity of instances. This paper is a significant step toward reducing false positive taboo decisions that over time harm minority communities. ⋆ This paper examines taboo language as a case study. The reader is cautioned that the paper contains strong language. † Equal Contribution. 1 Introduction Members of a community rely on a shared language for communication, one which evolves naturally, in situ, and is shaped by the norms and mores of the community (Gumperz, 1968). Norms promote Entitativity, the perception of group identity (Allan and Burridge, 2006). Norms may be explicit and codified into law or so subtle that while members may be unable to specify them they can still rec- ognize violations (Chandrasekharan et al., 2018). Utterances violating a these norms may be consid- ered taboo from that community’s perspective.1 Communities often self-regulate by censuring norm violating taboo language (Allan and Burridge, 2006). This censuring may be subtle such as ignor- ing taboo utterances leaving the individual some- what isolated and ineffective. Self-regulation could also be explicit and even severe such as expulsion from the community. Taboos are community-specific: Perceptions of taboos in language use are influenced by commu- nity (Allan and Burridge, 2006). Utterances that are benign in one community might be taboo in another2. E.g., ‘autistic’ is considered derogatory on most of reddit, its use usually leads to censure. However, on the subreddit /r/wallstreetbets, it is not and is instead used as a self-descriptor by mem- bers. The importance of considering the author’s community when studying taboo is emphasized in a recent critical survey …

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