揭示公众对AI的担忧与期待,发现风险感知决定接受度
Mapping Public Perception of Artificial Intelligence: Expectations, Risk-Benefit Tradeoffs, and Value As Determinants for Societal Acceptance
- 通过1100人问卷分析公众对AI能力的预期与评估
- 96.4%的价值判断由风险与收益感知决定,与可能性无关
- 适合政策制定者、开发者参考公众认知以推动技术落地
理解公众对人工智能(AI)的认知及其潜在风险与收益权衡至关重要,因这些认知可能影响政策制定、创新方向及社会接纳度。本研究基于德国1100名代表性受访者,考察了公众对AI未来能力的心理模型。参与者对71项关于自动驾驶、医疗、艺术、政治、战争及社会分裂等场景的陈述,评估其发生可能性、感知风险、潜在收益及整体价值。结果显示,尽管多数情景被视为可能发生,但普遍伴随高风险、低收益和低价值评价。在所有情景中,96.4%(r²=96.4%)的价值评估方差可由感知风险(β = -0.504)和感知收益(β = +0.710)解释,且与预期可能性无显著关联。人口统计学特征与人格特质影响风险、收益及整体评价,凸显提升AI素养与个性化信息传播的重要性。研究为研究人员、开发者与政策制定者提供了行动洞察,强调需关注公众关切与个体差异,以使AI发展契合社会价值观。
原文摘要 · Abstract (English)
Understanding public perception of artificial intelligence (AI) and the tradeoffs between potential risks and benefits is crucial, as these perceptions might shape policy decisions, influence innovation trajectories for successful market strategies, and determine individual and societal acceptance of AI technologies. Using a representative sample of 1100 participants from Germany, this study examines mental models of AI. Participants quantitatively evaluated 71 statements about AI's future capabilities (e.g., autonomous driving, medical care, art, politics, warfare, and societal divides), assessing the expected likelihood of occurrence, perceived risks, benefits, and overall value. We present rankings of these projections alongside visual mappings illustrating public risk-benefit tradeoffs. While many scenarios were deemed likely, participants often associated them with high risks, limited benefits, and low overall value. Across all scenarios, 96.4% ($r^2=96.4\%$) of the variance in value assessment can be explained by perceived risks ($β=-.504$) and perceived benefits ($β=+.710$), with no significant relation to expected likelihood. Demographics and personality traits influenced perceptions of risks, benefits, and overall evaluations, underscoring the importance of increasing AI literacy and tailoring public information to diverse user needs. These findings provide actionable insights for researchers, developers, and policymakers by highlighting critical public concerns and individual factors essential to align AI development with individual values.
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