发表机构
University of Toronto; McGill University; Mila Quebec AI Institute（多伦多大学; 麦吉尔大学; 米拉魁北克人工智能研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过对93名AI安全与伦理领域专家的调查及17名专家的访谈，分析了人类研究在AI安全与伦理领域的认知适配性与障碍，并提出相关建议以避免流于形式的“人类洗白”。
AI 中文摘要
AI技术在与人类交互过程中的安全风险日益凸显，当前评估这些风险的主流方法偏向技术手段，如模型基准（model benchmarks）和大语言模型（LLM）模拟，却常忽视以人为对象的实证研究。为探究这种对人类研究接受度的明显差距，我们对来自技术、社会技术、治理及规范背景的93名AI安全与伦理（AISE）研究人员开展了专家调查，并对17名研究人员进行了专家访谈。研究发现，尽管各方一致认为人类研究对AISE领域生成证据具有重要价值，但其应用与接受度却受限于感知到的有效性问题、实际资源障碍、对方法的认知及个人偏好，以及更广泛研究社区的基础设施限制。尤其值得注意的是，技术背景的研究人员往往对人类研究的重视程度较低，跨学科合作也较少，这表明他们对人类方法存在认知上的张力。我们提出了相关建议，旨在建立人类研究在AISE领域的认知适配性，弥合研究人员面临的阻碍性局限，同时避免流于形式的“人类洗白”（performative 'human-washing'）。
英文摘要
Safety risks of AI are becoming increasingly evident in human interactions with AI technologies. The prominent approaches to evaluating these risks favor technical methods, such as model benchmarks and LLM simulations, often sidelining empirical research with human subjects. To examine this apparent gap in the acceptance of human research, we conduct an expert survey (n=93) and expert interviews (n=17) with AI Safety & Ethics (AISE) researchers from Technical, Sociotechnical, Governance, and Normative backgrounds. Our findings suggest that although there is a consensus that human research is valuable for generating evidence for AISE, its adoption and acceptance are constrained by perceived validity issues, tangible resource barriers, epistemic and personal preferences in methods, and infrastructural constraints from the broader research community. In particular, Technical researchers tend to value human research less and collaborate across disciplines less, suggesting an epistemic tension towards human methods. We propose recommendations for establishing the epistemic fit of human research within AISE and bridging the prohibitive limitations that researchers face, while avoiding performative 'human-washing'.
CommentsNinth AAAI/ACM Conference on AI, Ethics, and Society (AIES 2026)
