🤖 AI Summary
研究通过分析982名参与者对17个AI和机器人挑战的评估，揭示了复杂性与准备度之间的关系，强调了在政策制定中应考虑具体挑战的差异。
📝 Abstract
Efforts to accelerate AI and robotics adoption require evidence about where communities are ready to act and where support is still needed. Yet averages across stakeholder groups can obscure relationships that emerge when the same person evaluates different challenges. We analyse a repeated card-based survey in which 982 participants provided 15,200 evaluations of 17 AI and robotics challenges. Each challenge was rated on 1-5 measures of significance, complexity and readiness, where readiness refers to perceived community preparedness and available resources rather than personal competence or realised adoption. Because participants evaluated multiple challenges, the design separates stable between-person differences from challenge-specific within-person deviations. Within the same respondent, a challenge rated one point more complex than usual is associated with about 0.21 points lower readiness (p less than 0.001). By contrast, respondents who generally rate challenges as more complex do not systematically report lower readiness (p=0.29). Significance is positively associated with readiness, while unusually high complexity modestly weakens this alignment. These relationships vary across challenge families, and professional background remains associated with adjusted preparedness. On applied cards, confidence, trust and related perceptions add substantial information about readiness, including for held-out participants. For policymakers and organisations, averaging across stakeholders can hide challenge-specific barriers. Readiness assessments should preserve both differences between stakeholder groups and variation within the same stakeholders across challenges. Effective adoption and literacy strategies should ask not only who appears ready, but which challenges they find unusually difficult and whether the likely constraint concerns implementation, capability, assurance or resources.