生成式人工智能可能强化软件工程教育中的社会偏见

中文摘要 AI 辅助

生成式人工智能（GenAI）正日益广泛地部署于各类现实应用之中。若缺乏审慎评估，对这些系统的依赖可能引发意外后果，例如强化刻板印象和放大社会偏见。此类风险在教育环境中尤为突出，因为早期决策可能塑造学生的兴趣、机会和职业轨迹。本文中，我们探讨软件工程教师使用GenAI可能无意中强化软件特定社会偏见的问题。我们聚焦于两个代表性任务：基于学生档案的团队组建，以及为教育材料生成视觉内容。我们的结果揭示了两项任务中的显著偏见。在团队组建中，性别和国籍等因素影响角色分配（例如，女性比同等资质的男性更可能被分配到前端角色）。在视觉内容生成任务中，模型在描绘群体时通常产生多样且均衡的表示。然而，在生成单个人物图像时，输出结果以男性和浅肤色为主。这些发现凸显了生成模型中人口统计学偏见的持续存在，并强调了在支持其在教育环境中负责任使用方面，需要领域特定的评估和缓解策略。

英文摘要

Generative artificial intelligence (GenAI) is increasingly being deployed across a wide range of real-world applications. Without careful evaluation, reliance on these systems can have unintended consequences, such as reinforcement of stereotypes and amplification of social biases. Such risks are particularly important in educational settings, where early decisions can shape students' interests, opportunities, and career trajectories. In this paper, we investigate how GenAI use by software engineering instructors may inadvertently reinforce software-specific social biases. We focus on two representative tasks: team formation based on student profiles and the generation of visual content for educational materials. Our results reveal significant biases in both tasks. In team formation, factors such as gender and nationality affect role assignments (e.g., women are more likely to be assigned to front-end roles than equally qualified men). In the visual content generation task, models generally produce diverse and balanced representations when depicting groups of individuals. However, when generating images of a single person, the outputs are predominantly male and light-skinned. These findings highlight the persistence of demographic biases in generative models and underscore the need for domain-specific evaluation and mitigation strategies to support their responsible use in educational settings.

发表机构

• University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）

机构由 AI 辅助整理，请以论文原文为准。