Public sector use of AI has been quietly on the rise for the past decade, but only recently have efforts to regulate it entered the cultural zeitgeist. While simple to articulate, promoting ethical and effective roll outs of AI systems in government is a notoriously elusive task. On the one hand there are hard-to-address pitfalls associated with AI-based tools, including concerns about bias towards marginalized communities, safety, and gameability. On the other, there is pressure not to make it too difficult to adopt AI, especially in the public sector which typically has fewer resources than the private sector$\unicode{x2014}$conserving scarce government resources is often the draw of using AI-based tools in the first place. These tensions create a real risk that procedures built to ensure marginalized groups are not hurt by government use of AI will, in practice, be performative and ineffective. To inform the latest wave of regulatory efforts in the United States, we look to jurisdictions with mature regulations around government AI use. We report on lessons learned by officials in Brazil, Singapore and Canada, who have collectively implemented risk categories, disclosure requirements and assessments into the way they procure AI tools. In particular, we investigate two implemented checklists: the Canadian Directive on Automated Decision-Making (CDADM) and the World Economic Forum's AI Procurement in a Box (WEF). We detail three key pitfalls around expertise, risk frameworks and transparency, that can decrease the efficacy of regulations aimed at government AI use and suggest avenues for improvement.

翻译：过去十年中，公共部门对人工智能的使用悄然增长，但直到最近，相关监管努力才进入文化主流。尽管表述简单，但促进政府中人工智能系统的道德和有效部署却是一项众所周知的艰巨任务。一方面，基于人工智能的工具存在难以应对的陷阱，包括对边缘化群体的偏见、安全性以及可操纵性等担忧。另一方面，也存在不应使人工智能采纳过于困难的压力——尤其是在通常资源少于私营部门的公共部门，节约稀缺的政府资源往往正是使用基于人工智能工具的首要动因。这些矛盾造成了一种真实风险：旨在确保边缘化群体不受政府人工智能使用伤害的程序，在实践中可能流于形式且无效。为了给美国最新一轮监管努力提供参考，我们借鉴了在政府人工智能使用方面具有成熟法规的司法管辖区。我们报告了巴西、新加坡和加拿大官员的经验教训，他们共同将风险类别、披露要求和评估纳入人工智能工具的采购流程。特别是，我们调查了两项已实施的清单：加拿大的《自动化决策指令》（CDADM）和世界经济论坛的《人工智能采购工具箱》（WEF）。我们详述了围绕专业知识、风险框架和透明度的三个关键陷阱——这些陷阱可能削弱旨在规范政府人工智能使用的法规效力——并提出了改进方向。