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.

通过自动生成的针对性别刻板印象的对立刻板印象，自称为男性和女性的研究参与者评估其冒犯性、可信度和潜在有效性，并发现对立事实和扩大普遍性（即无论群体成员如何都有某种特质）的策略是最有效的对策，相比较而言，幽默、换位思考、对立例子和对发言者的同理心等策略被认为效果较差。同时，对不同目标的刻板印象而言，评级的差异更明显，而对评级者的性别差异相对较小。然而，令人震惊的是，许多通过 AI 生成的对立刻板印象被认为具有冒犯性和 / 或不可信。我们的分析和收集到的数据集为对抗在线互动中的性别刻板印象提供了基础洞察，指导未来努力开发有效挑战性别刻板印象的策略。

挑战负面性别刻板印象：自动对抗刻板印象有效性研究

Challenging Negative Gender Stereotypes: A Study on the Effectiveness of  Automated Counter-Stereotypes

Emojis, which encapsulate semantics beyond mere words or phrases, have become
prevalent in social network communications. This has spurred increasing
scholarly interest in exploring their attributes and functionalities. However,
emoji-related research and application face two primary challenges. First,
researchers typically rely on crowd-sourcing to annotate emojis in order to
understand their sentiments, usage intentions, and semantic meanings. Second,
subjective interpretations by users can often lead to misunderstandings of
emojis and cause the communication barrier. Large Language Models (LLMs) have
achieved significant success in various annotation tasks, with ChatGPT
demonstrating expertise across multiple domains. In our study, we assess
ChatGPT's effectiveness in handling previously annotated and downstream tasks.
Our objective is to validate the hypothesis that ChatGPT can serve as a viable
alternative to human annotators in emoji research and that its ability to
explain emoji meanings can enhance clarity and transparency in online
communications. Our findings indicate that ChatGPT has extensive knowledge of
emojis. It is adept at elucidating the meaning of emojis across various
application scenarios and demonstrates the potential to replace human
annotators in a range of tasks.

ChatGPT 评估了其在已注释和后续任务处理方面的有效性，以验证 ChatGPT 能否在表情符号研究中作为可行的替代品，并且其解释表情符号含义的能力能增加在线沟通的清晰度和透明度。研究结果表明，ChatGPT 对表情符号有广泛的知识，并能够在不同应用场景中阐明其含义，具备取代人类注释者进行多种任务的潜力。