The widespread adoption of large language models (LLMs) and generative AI (GenAI) tools across diverse applications has amplified the importance of addressing societal biases inherent within these technologies. While the NLP community has extensively studied LLM bias, research investigating how non-expert users perceive and interact with biases from these systems remains limited. As these technologies become increasingly prevalent, understanding this question is crucial to inform model developers in their efforts to mitigate bias. To address this gap, this work presents the findings from a university-level competition, which challenged participants to design prompts for eliciting biased outputs from GenAI tools. We quantitatively and qualitatively analyze the competition submissions and identify a diverse set of biases in GenAI and strategies employed by participants to induce bias in GenAI. Our finding provides unique insights into how non-expert users perceive and interact with biases from GenAI tools.

本研究解决了当前对生成性人工智能（GenAI）中固有社会偏见理解不足的问题，重点考察非专业用户如何识别和与这些偏见互动。通过分析大学级竞赛中参与者的提示设计，我们发现了多种在GenAI中存在的偏见及其诱导策略，为理解用户对GenAI系统偏见的感知提供了重要见解，旨在为模型开发者的偏见缓解工作提供指导。

嘿，GPT，你能更种族主义吗？对众包尝试从生成性人工智能中引出偏见内容的分析