调研93位专家，发现人因研究在AI安全中受阻
Studying People to Study AI: Expert Perspectives on the Epistemic Fit and Barriers of Human Research in AI Safety & Ethics
- 通过问卷与访谈，分析专家对人类研究的接受度
- 技术背景专家更轻视人因研究，存在认知分歧
- 建议建立方法适配性，避免形式化的人类参与
AI安全风险在人机交互中日益显现。当前评估多依赖技术手段（如模型基准、大模型模拟），忽视面向真实人类的实证研究。为探究这一现象背后的原因，我们对93位来自技术、社会技术、治理与规范等领域的AI安全与伦理（AISE）专家进行了问卷调查，并对17位专家进行深度访谈。结果表明，尽管普遍认可人类研究对生成证据的价值，但其采纳仍受限于方法有效性质疑、资源不足、认识论偏好及科研生态支持缺失等问题。尤其技术背景研究者更倾向于低估人因研究价值，且跨学科协作意愿较低，反映出深层次的方法论张力。论文提出建议以增强人类研究在AISE中的认识论契合度，克服实际障碍，同时警惕表面化的‘人类洗白’行为。
原文摘要 · Abstract (English)
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'.
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