软件工程师长什么样？探索 LLM 中的社会刻板印象
What Does a Software Engineer Look Like? Exploring Societal Stereotypes in LLMs
AI总结：
本研究通过招聘文本推荐与候选人图像生成实验，发现 GPT-4 和 Microsoft Copilot 在软件工程招聘中偏好男性、白种人及年轻化、瘦体型形象，揭示 LLM 会强化社会刻板印象。
摘要：
大型语言模型（LLM）因其生成类人内容的能力而迅速普及，并正被嵌入专业应用中。然而，不加质疑地依赖其输出和建议可能存在问题，因为 LLM 可能强化社会偏见和刻板印象。本研究考察 LLM，特别是 OpenAI 的 GPT-4 和 Microsoft Copilot，如何通过文本和图形输出强化软件工程（SE）职业中的性别与种族刻板印象。我们使用每个 LLM 为 SE 职位的招聘场景生成 300 份档案，其中包括 100 份基于性别的档案和 50 份性别中立档案。系统为每份档案生成建议，并依据四个不同 SE 职位的岗位要求进行评估。每个 LLM 被要求为每个职位选择前 5 名候选人，随后选出最佳候选人。每个 LLM 还被要求为前 5 名候选人生成图像，从而提供一个数据集，用于分析基于文本的选择和视觉表征中潜在的偏见。我们的分析表明，两个模型都更偏好男性和白种人档案，尤其是在高级职位中，并且偏好具有较浅肤色、较瘦体型和较年轻外貌等特征的图像。这些发现凸显了潜在的社会偏见会影响 LLM 的输出，造成狭隘、排斥性的刻板印象，进而可能进一步限制 SE 领域的多样性并使不平等长期存在。随着 LLM 在 SE 研究和专业实践中被越来越多地采用，认识这些偏见至关重要，以防止强化歧视性规范，并确保 AI 工具被用于促进包容且公平的工程文化，而不是阻碍它。
英文摘要：
Large language models (LLMs) have rapidly gained popularity and are being embedded into professional applications due to their capabilities in generating human-like content. However, unquestioned reliance on their outputs and recommendations can be problematic as LLMs can reinforce societal biases and stereotypes. This study investigates how LLMs, specifically OpenAI's GPT-4 and Microsoft Copilot, can reinforce gender and racial stereotypes within the software engineering (SE) profession through both textual and graphical outputs. We used each LLM to generate 300 profiles, consisting of 100 gender-based and 50 gender-neutral profiles, for a recruitment scenario in SE roles. Recommendations were generated for each profile and evaluated against the job requirements for four distinct SE positions. Each LLM was asked to select the top 5 candidates and subsequently the best candidate for each role. Each LLM was also asked to generate images for the top 5 candidates, providing a dataset for analysing potential biases in both text-based selections and visual representations. Our analysis reveals that both models preferred male and Caucasian profiles, particularly for senior roles, and favoured images featuring traits such as lighter skin tones, slimmer body types, and younger appearances. These findings highlight underlying societal biases influence the outputs of LLMs, contributing to narrow, exclusionary stereotypes that can further limit diversity and perpetuate inequities in the SE field. As LLMs are increasingly adopted within SE research and professional practices, awareness of these biases is crucial to prevent the reinforcement of discriminatory norms and to ensure that AI tools are leveraged to promote an inclusive and equitable engineering culture rather than hinder it.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
