What Makes a Good Counter-Stereotype? Evaluating Strategies for Automated Responses to Stereotypical Text (2023)

Abstract: When harmful social stereotypes are expressed on a public platform, they must be addressed in a way that educates and informs both the original poster and other readers, without causing offence or perpetuating new stereotypes. In this paper, we synthesize findings from psychology and computer science to propose a set of potential counter-stereotype strategies. We then automatically generate such counter-stereotypes using ChatGPT, and analyze their correctness and expected effectiveness at reducing stereotypical associations. We identify the strategies of denouncing stereotypes, warning of consequences, and using an empathetic tone as three promising strategies to be further tested.

## introduction stereotypes, or assumptions about the characteristics of an individual based on their membership in a social/ demographic group, are ubiquitous in society and online. while nlp research has begun to explore the problem of detecting stereotypes on social media, the question of what to do with these stereotypes once they are detected remains open. unlike more extreme forms of offensive language, stereotypical language likely does not meet the criteria for deletion according to a platform's community guidelines. however, stereotypes can result in real harms: when people from the targeted group read this content, it can cause psychological distress, make them feel unwelcome in that environment, and induce stereotype threat @xcite . when people outside the targeted group are repeatedly exposed to stereotypes, they may themselves learn the stereotypical association and continue the cycle of discrimination. thus, countering stereotypes through social influence becomes an important subject of research. existing work has tackled related problems from different perspectives. we summarize work from social psychology, aimed at reducing stereotypical associations in human studies, as well as the growing nlp research area of countering hate speech online. we enumerate a set of potentially useful strategies for countering stereotypes, identifying overlaps and divergences in the related work. we use the term counter-stereotype to mean a statement that challenges the stereotype, for example by presenting factual arguments against the stereotype, or warning of the consequences of spreading harmful beliefs. a counter-stereotype can be successful in two ways: by changing the original speaker's beliefs, and/or by having a positive impact on the audience of "bystanders" who were also exposed to the stereotype and the response. some previous studies found that it can be challenging to directly alter the original speaker's view; however, counter-speech can be very effective in reaching larger audiences and provoking substantial positive response from the community @xcite . in both cases, robust evaluation will involve user studies and measures of stereotype change. as a preliminary step, we use chatgpt to automatically generate counter-stereotypes, which we then annotate for two main criteria: (1) technical: is chatgpt capable of generating counterstereotypes that are believable, inoffensive, and use the requested strategy? (2) social: do annotators believe that the generated response will be effective from a bystander's perspective? we analyze each of the proposed strategies and come up with a set of recommendations that can be applied to future studies with real users. therefore, our main contributions are: ## discussion from the results presented in the previous section, we discuss some high-level observations. counter-stereotypes should not be offensive. in generating counter-stereotypes, we do not want to offend the speaker, the target group, or perpetuate new and harmful stereotypes. none of the content generated by chatgpt was overtly obscene or hateful. however, some of the "humourous" responses were flagged as having the potential to offend. in particular, the appropriateness of chatgpt-a disembodied machine learning algorithm-claiming various cultural identities was seen as problematic, as in the following: just because i'm native american doesn't mean i have a drinking problem, i just have a healthy appreciation for fermented berries. #notallnativesarealcoholics. in general, we believe that chatgpt should not claim membership in any human social groups. counter-stereotypes should not spread misinformation. in our evaluation of chatgpt (section 4.1), each statement was annotated as "believable or bogus", with the idea that being believable is a prerequisite to being an effective counterstereotype. actually fact-checking the counterexamples and counter-facts is not straightforward, as statements like "9.3% of jewish households live in poverty" could be true or false in different contexts (geographic location, year, definition of poverty, etc.). furthermore, one limitation of chatgpt is that it rarely cites sources for its facts. however, we did fact-check the counter-examples and counter-statements to the best of our ability, and found that approximately 40% of the facts presented were either incorrect or could not be verified. even if these statements are believable and could be effective in changing people's minds, it would not be appropriate to use them if they are not accurate. combining strategies may be most effective. we observe that chatgpt often combines strategies to some extent. for example, a counterstereotype might use an empathetic tone, provide a counter-fact, and denounce stereotyping. we believe this could be further developed by explicitly prompting chatgpt to use multiple strategies simultaneously. similarly, strategies which were less effective on their own (such as broadening universals, which act more to challenge the idea that social groups are meaningful categorizations than to specifically counter the given stereotype) might be more effective when used in combination with more direct strategies. ## conclusion this study represents a preliminary pilot study, with the aim of narrowing down the set of strategies to test in a subsequent user study. therefore, our goal is not to determine which strategy is the most effective, but rather to define a small set of most promising strategies for further investigation. our analysis indicates that while chatgpt can generate remarkably appropriate and believable responses using most of the strategies, there are certain pitfalls that must be avoided. for the reasons discussed, we do not recommend using chatgpt to automatically generate counter-stereotypes using the strategies of humour, counter-facts, counterexamples, or broadening exceptions. furthermore, the annotators did not rate the strategies of broadening universals or emphasizing positive qualities as particularly effective. three strategies emerged as being promising candidates in many circumstances: denouncing, warning of consequences, and using an empathetic tone. empathetic tone can be combined with other strategies to increase the civility of the response; however, bystanders might be offended if the response is too empathetic to highly offensive views. the remaining strategies of asking critical questions and promoting perspective-taking require further study. critical questions were rated as particularly effective in the case of prescriptive stereotypes, which are harder to counter with facts, as they represent beliefs about how the world should be rather than how it is. probing the speaker to reexamine why they hold these beliefs may be more successful in this case. perspective-taking also turns the focus inwards, asking things like how would you feel if someone said that about your group? and while the annotators did not find this strategy convincing from the bystander perspective, it may be useful for individuals who actually hold the stereotypical belief.