  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.
