AI社会研究虽仍需跨学科，但纯计算机团队正主导社会议题产出。
Societal AI Research Has Become Less Interdisciplinary
- 分析万篇arXiv论文，量化社会议题在技术论文中的体现程度。
- 纯计算机团队的社会类论文占比上升，覆盖公平、安全、医疗等多领域。
- 挑战了跨学科主导社会AI的假设，引发对技术团队角色的反思。
随着人工智能系统深度融入日常生活，推动其发展与伦理及社会价值对齐的呼声日益高涨。跨学科合作常被视为实现这一目标的关键路径。然而，目前尚不清楚跨学科研究团队是否真正引领了实践中的这一转变。本研究分析了2014至2024年间发表于arXiv的超10万篇人工智能相关论文，探讨伦理价值与社会关切如何融入技术性AI研究。我们构建分类器识别社会相关内容，并测量其在论文中的表达程度。研究发现显著趋势：尽管跨学科团队仍更可能产出关注社会的研究，但仅由计算机科学团队组成的团队如今已占该领域整体社会性产出的更大份额。这些团队正越来越多地将社会关切融入研究中，并涵盖从公平性与安全性到医疗健康和虚假信息等广泛领域。这一发现挑战了关于社会型AI驱动因素的普遍认知，引发重要问题：若多数社会导向研究由纯技术团队完成，这对新兴的人工智能安全与治理理解意味着什么？对于社会科学与人文学科学者而言，在一个日益回应社会需求的技术领域中，我们还能提供何种独特视角来塑造人工智能的未来？
原文摘要 · Abstract (English)
As artificial intelligence (AI) systems become deeply embedded in everyday life, calls to align AI development with ethical and societal values have intensified. Interdisciplinary collaboration is often championed as a key pathway for fostering such engagement. Yet it remains unclear whether interdisciplinary research teams are actually leading this shift in practice. This study analyzes over 100,000 AI-related papers published on ArXiv between 2014 and 2024 to examine how ethical values and societal concerns are integrated into technical AI research. We develop a classifier to identify societal content and measure the extent to which research papers express these considerations. We find a striking shift: while interdisciplinary teams remain more likely to produce societally-oriented research, computer science-only teams now account for a growing share of the field's overall societal output. These teams are increasingly integrating societal concerns into their papers and tackling a wide range of domains - from fairness and safety to healthcare and misinformation. These findings challenge common assumptions about the drivers of societal AI and raise important questions. First, what are the implications for emerging understandings of AI safety and governance if most societally-oriented research is being undertaken by exclusively technical teams? Second, for scholars in the social sciences and humanities: in a technical field increasingly responsive to societal demands, what distinctive perspectives can we still offer to help shape the future of AI?
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