研究美国城市如何通过旧采购制度影响AI选择与决策权
Legacy Procurement Practices Shape How U.S. Cities Govern AI: Understanding Government Employees' Practices, Challenges, and Needs
- 分析19名城市员工访谈，揭示旧采购流程如何决定买什么AI
- 发现现有改革未能解决算法偏见、透明度等三大核心挑战
- 为政策制定者提供预防公共AI风险的实践路径
大多数政府采用的AI工具并非自行开发，而是通过公共采购从第三方供应商获得。本文首次对美国城市采购实践如何影响公共部门AI的关键决策进行实证研究。通过对7个美国城市的19名负责AI采购的城市员工开展半结构化访谈，我们发现，受数十年法律和规范影响的既有采购制度，构成了决定购买哪些AI以及由谁掌握决策权的基础框架。基于员工对实际采购案例的反思，我们识别出三个关键挑战，这些挑战虽推动改革但尚未被现有倡议充分解决。据此，本文讨论了对FAccT社区的启示与机遇，以支持城市在采购全周期中预见并防范AI风险。
原文摘要 · Abstract (English)
Most AI tools adopted by governments are not developed internally, but instead are acquired from third-party vendors in a process called public procurement. In this paper, we conduct the first empirical study of how United States cities' procurement practices shape critical decisions surrounding public sector AI. We conduct semi-structured interviews with 19 city employees who oversee AI procurement across 7 U.S. cities. We found that cities' legacy procurement practices, which are shaped by decades-old laws and norms, establish infrastructure that determines which AI is purchased, and which actors hold decision-making power over procured AI. We characterize the emerging actions cities have taken to adapt their purchasing practices to address algorithmic harms. From employees' reflections on real-world AI procurements, we identify three key challenges that motivate but are not fully addressed by existing AI procurement reform initiatives. Based on these findings, we discuss implications and opportunities for the FAccT community to support cities in foreseeing and preventing AI harms throughout the public procurement processes.
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