Challenging Negative Gender Stereotypes: A Study on the Effectiveness of Automated Counter-Stereotypes
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 counter-act 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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Revisiting The Classics: A Study on Identifying and Rectifying Gender Stereotypes in Rhymes and Poems
Rhymes and poems are a powerful medium for transmitting cultural norms and societal roles. However, the pervasive existence of gender stereotypes in these works perpetuates biased perceptions and limits the scope of indi…
Language ModelingLanguage ModellingLarge Language ModelShirtless and Dangerous: Quantifying Linguistic Signals of Gender Bias in an Online Fiction Writing Community
Imagine a princess asleep in a castle, waiting for her prince to slay the dragon and rescue her. Tales like the famous Sleeping Beauty clearly divide up gender roles. But what about more modern stories, borne of a genera…
LLMs Reproduce Stereotypes of Sexual and Gender Minorities
A large body of research has found substantial gender bias in NLP systems. Most of this research takes a binary, essentialist view of gender: limiting its variation to the categories _men_ and _women_, conflating gender …
Text GenerationGender Stereotypes Differ between Male and Female Writings
Written language often contains gender stereotypes, typically conveyed unintentionally by the author. To study the difference in how female and male authors portray people of different genders, we quantitatively evaluate…
A Closer Look on Gender Stereotypes in Movie Recommender Systems and Their Implications with Privacy
The movie recommender system typically leverages user feedback to provide personalized recommendations that align with user preferences and increase business revenue. This study investigates the impact of gender stereoty…
AttributeRecommendation Systems