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Challenging negative gender stereotypes: a study on the effectiveness of automated counter-stereotypes

From National Research Council Canada

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Linkhttps://aclanthology.org/2024.lrec-main.268
AuthorSearch for: 1; Search for: 1; Search for: ; Search for: 1
Affiliation
  1. National Research Council Canada. Digital Technologies
FormatText, Article
Conference2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), May 20-25, 2024, Torino, Italia
Subjectgender stereotypes; counter-stereotypes; social influence; online conversations
Abstract
Gender stereotypes are pervasive beliefs about individuals based on their gender that play a significant role in shaping societal attitudes, behaviours, and even opportunities. Recognizing the negative implications of gender stereotypes, particularly in online communications, this study investigates eleven strategies to automatically counteract and challenge these views. We present AI-generated gender-based counter-stereotypes to (self-identified) male and female study participants and ask them to assess their offensiveness, plausibility, and potential effectiveness. The strategies of counter-facts and broadening universals (i.e., stating that anyone can have a trait regardless of group membership) emerged as the most robust approaches, while humour, perspective-taking, counter-examples, and empathy for the speaker were perceived as less effective. Also, the differences in ratings were more pronounced for stereotypes about the different targets than between the genders of the raters. Alarmingly, many AI-generated counter-stereotypes were perceived as offensive and/or implausible. Our analysis and the collected dataset offer foundational insight into counter-stereotype generation, guiding future efforts to develop strategies that effectively challenge gender stereotypes in online interactions.
Publication date
PublisherELRA Language Resource Association
International Committee on Computational Linguistics (ICCL)
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  • Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024) (May 2024): 3005–3015.
LanguageEnglish
Peer reviewedYes
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Record identifier571ee634-4f7f-4039-a5a9-0595dcdb1216
Record created2024-05-24
Record modified2024-05-27
Date modified: