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

Par Conseil national de recherches du Canada

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Lienhttps://aclanthology.org/2024.lrec-main.268
AuteurRechercher : 1; Rechercher : 1; Rechercher : ; Rechercher : 1
Affiliation
  1. Conseil national de recherches du Canada. Technologies numériques
FormatTexte, Article
Conférence2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), May 20-25, 2024, Torino, Italia
Sujetgender stereotypes; counter-stereotypes; social influence; online conversations
Résumé
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.
Date de publication
Maison d’éditionELRA Language Resource Association
International Committee on Computational Linguistics (ICCL)
Licence
Dans
  • Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024) (mai 2024) : 3005–3015.
Langueanglais
Publications évaluées par des pairsOui
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Identificateur de l’enregistrement571ee634-4f7f-4039-a5a9-0595dcdb1216
Enregistrement créé2024-05-24
Enregistrement modifié2024-05-27
Date de modification :