arXiv:2412.01459cs.CYcs.AI2024-12被引 2

专家与公众对AI风险收益认知差异大,影响社会接受度。

Perception Gaps in Risk, Benefit, and Value Between Experts and Public Challenge Socially Accepted AI

  • 对比1110名公众与119名专家在71个场景下的评估
  • 专家更乐观:预测更高概率、更低风险、更高收益
  • 揭示治理与沟通关键分歧点,助力可信AI发展

人工智能正重塑诸多社会领域,引发对其风险、收益及公众与学术界认知错位的关切。本研究考察了1110名普通公众(受AI影响者)与119名学术AI专家(技术塑造者)在71个场景中对AI能力与影响的感知,涵盖可持续性、医疗、就业、社会不平等、艺术与战争等领域。参与者通过心理测量模型评估四个维度:发生可能性、感知风险与收益、整体价值(或情感)。结果表明,专家普遍预期更高概率、感知更低风险、报告更大收益并表达更积极情感。此外,两组权重不同:专家比公众更轻视风险而更看重收益。可视化评估图显示部分共识(如医疗诊断、犯罪应用)和明显张力点(如法律判决、政治决策),提示需加强沟通与政策干预。研究揭示核心转化挑战:若要使AI研发与部署契合社会优先事项,必须理解并弥合开发者与公众间的认知鸿沟。结果为价值敏感型AI治理与跨利益相关方信任建设提供实证基础。

原文摘要 · Abstract (English)

Artificial Intelligence (AI) is reshaping many societal domains, raising critical questions about its risks, benefits, and the potential misalignment between public and academic perspectives. This study examines how the general public (N=1110) -- individuals who interact with or are impacted by AI technologies -- and academic AI experts (N=119) -- those elites shaping AI development -- perceive AI's capabilities and impact across 71 scenarios. These scenarios span domains such as sustainability, healthcare, job performance, societal inequality, art, and warfare. Participants evaluated these scenarios across four dimensions using the psychometric model: likelihood, perceived risk and benefit, and overall value (or sentiment). The results suggest significant differences: experts consistently anticipate higher probabilities, perceive lower risks, report greater benefits, and express more positive sentiment toward AI compared to the non-experts. Moreover, both groups apply different weighting schemes: experts discount risk more heavily relative to benefit than non-experts. Visual mappings of these evaluations uncover areas convergent evaluations (e.g., AI performing medical diagnoses or criminal use) as well as tension points (e.g., decision of legal cases, political decision making), highlighting areas where communication and policy interventions may be needed. These findings underscore a critical translational challenge: if AI research and deployment are to align with societal priorities, the perception gap between developers and the public must be better understood and addressed. Our results provide an empirical foundation for value-sensitive AI governance and trust-building strategies across stakeholder groups.

AI治理认知差异公众信任

Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品;中文卡片由大模型生成,请以原文为准。