比较251项研究，揭示不同领域公众对AI态度的差异与成因。
Perceptions of AI Across Sectors: A Comparative Review of Public Attitudes
- 系统梳理2011-2025年251项研究，跨领域分析公众对AI的态度。
- 发现技术性能之外，制度信任、公平感与伦理关切显著影响认知。
- 适用于政策制定者与AI治理研究者，推动分场景负责任发展。
本文基于对2011至2025年间251项关于公众对人工智能态度的研究进行领域导向的比较综述。通过系统文献回顾，分析了感知收益与担忧（或风险）等多重因素如何影响公众在医疗、教育、安全、公共管理、生成式AI及自动驾驶等领域的接受度或抵制情绪。研究揭示了个体、情境与技术因素的重复模式，同时追踪了机构信任、感知公平性与伦理关切的差异。结果表明，公众对AI的认知不仅受技术设计或性能影响，还深受具体领域特征、社会想象、文化叙事与历史遗产塑造。该比较视角为制定更具针对性和情境敏感性的负责任AI治理策略提供了基础。
原文摘要 · Abstract (English)
This paper offers a domain-mediated comparative review of 251 studies on public attitudes toward AI, published between 2011 and 2025. Drawing on a systematic literature review, we analyse how different factors including perceived benefits and concerns (or risks) shape public acceptance of - or resistance to - artificial intelligence across domains and use-cases, including healthcare, education, security, public administration, generative AI, and autonomous vehicles. The analysis highlights recurring patterns in individual, contextual, and technical factors influencing perception, while also tracing variations in institutional trust, perceived fairness, and ethical concerns. We show that the public perception in AI is shaped not only by technical design or performance but also by sector-specific considerations as well as imaginaries, cultural narratives, and historical legacies. This comparative approach offers a foundation for developing more tailored and context-sensitive strategies for responsible AI governance.
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