用5个道德维度预测公众对AI应用的接受度
What Makes AI Applications Acceptable or Unacceptable? A Predictive Moral Framework
- 基于风险、收益等5个道德维度构建可预测框架
- 该框架解释了90%以上的接受度差异，准确率高
- 适合政策制定者和开发者提前规避公众抵制
随着人工智能快速改变社会，开发者与政策制定者难以预判哪些应用会遭遇公众道德抵制。我们提出，这些判断并非随意，而是系统且可预测的。在一项大规模预注册研究中（N = 587，美国代表性样本），我们使用涵盖个人与组织场景的100种AI应用分类体系——包括功能用途及对AI自身的道德对待。参与者集体评估显示，应用接受度从高度不可接受到完全可接受不等。研究发现，这种差异由五个核心道德特质——感知风险、效益、欺骗性、非自然性、问责减少——共同解释超过90%的接受度方差。该框架在所有领域均表现强劲，成功预测了未见应用的个体判断。结果表明，公众对新技术的评价背后存在结构化道德心理，为预见公众抵制、推动负责任的AI创新提供了有力工具。
原文摘要 · Abstract (English)
As artificial intelligence rapidly transforms society, developers and policymakers struggle to anticipate which applications will face public moral resistance. We propose that these judgments are not idiosyncratic but systematic and predictable. In a large, preregistered study (N = 587, U.S. representative sample), we used a comprehensive taxonomy of 100 AI applications spanning personal and organizational contexts-including both functional uses and the moral treatment of AI itself. In participants' collective judgment, applications ranged from highly unacceptable to fully acceptable. We found this variation was strongly predictable: five core moral qualities-perceived risk, benefit, dishonesty, unnaturalness, and reduced accountability-collectively explained over 90% of the variance in acceptability ratings. The framework demonstrated strong predictive power across all domains and successfully predicted individual-level judgments for held-out applications. These findings reveal that a structured moral psychology underlies public evaluation of new technologies, offering a powerful tool for anticipating public resistance and guiding responsible innovation in AI.
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