构建政府负责任用AI的数据挑战分类体系，助力识别高风险部署
Responsible AI Adoption in the Public Sector: A Data-Centric Taxonomy of AI Adoption Challenges
- 基于43项研究与21位专家评估，提炼出13个数据相关挑战
- 涵盖数据质量差、基础设施不足、治理薄弱等关键问题
- 为政策制定者提供诊断工具，推动可信AI落地
尽管人工智能在公共服务、决策和行政效率方面具有变革潜力，但其应用仍不均衡，受技术、组织和制度多重挑战制约。负责任AI框架强调公平性、可问责性和透明性，契合可信AI与公平AI原则，但多停留在理想层面，忽视了数据基础与制度现实。本研究通过系统回顾43项研究及21次专家评估，构建了政府负责任AI采纳中的数据相关挑战分类体系，涵盖技术、组织与环境维度的13个核心挑战，包括数据质量差、缺乏AI就绪基础设施、治理薄弱、人机决策错配、经济与环境可持续性担忧等。该分类体系标注了制度压力，可作为诊断工具，揭示高风险AI部署的‘症状’，指导政策制定者建立必要的制度与数据治理条件，推动负责任的AI采纳。
原文摘要 · Abstract (English)
Despite Artificial Intelligence (AI) transformative potential for public sector services, decision-making, and administrative efficiency, adoption remains uneven due to complex technical, organizational, and institutional challenges. Responsible AI frameworks emphasize fairness, accountability, and transparency, aligning with principles of trustworthy AI and fair AI, yet remain largely aspirational, overlooking technical and institutional realities, especially foundational data and governance. This study addresses this gap by developing a taxonomy of data-related challenges to responsible AI adoption in government. Based on a systematic review of 43 studies and 21 expert evaluations, the taxonomy identifies 13 key challenges across technological, organizational, and environmental dimensions, including poor data quality, limited AI-ready infrastructure, weak governance, misalignment in human-AI decision-making, economic and environmental sustainability concerns. Annotated with institutional pressures, the taxonomy serves as a diagnostic tool to surface 'symptoms' of high-risk AI deployment and guides policymakers in building the institutional and data governance conditions necessary for responsible AI adoption.
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