Why Public Service AI Governance Frameworks Risk Failing in the Age of General-Purpose AI: Lessons from Policing
作者: Sam Relins, Daniel Birks
分类: cs.CY, cs.AI
发布日期: 2026-07-28
备注: Preprint; submitted for peer review
💡 一句话要点
提出公共服务AI治理框架以应对通用AI挑战
🎯 匹配领域: 支柱九：具身大模型 (Embodied Foundation Models)
关键词: 公共服务 人工智能 通用AI 治理框架 警务 安全性 政策建议
📋 核心要点
- 现有公共服务AI治理框架在应对通用AI的挑战时，面临准确性、偏见和可解释性等问题的严重不足。
- 论文提出在治理文档中明确狭义与通用AI的分类，并暂停GPAI在警务中的部署，直到安全性得到验证。
- 通过对警务领域的案例分析，展示了治理失败的严重后果，并指出其他公共服务领域可能面临类似问题。
📝 摘要（中文）
公共服务面临着越来越大的压力，需要采用人工智能（AI）来弥补需求上升与资源下降之间的差距。随着通用AI（GPAI）的出现，这种压力愈加明显。本文指出，GPAI的通用性、可及性和低部署成本削弱了历史上追求AI安全的条件。公共服务治理框架所强调的安全概念，如准确性、偏见、可解释性和问责制，都是基于狭义、专用AI而设计的，而GPAI的特性使得这些概念的实现变得困难。以警务为例，治理失败的后果最为严重，本文建议在治理文档中明确狭义和通用AI的分类，暂停在警务中部署GPAI，直到有足够的证据证明其安全性。
🔬 方法详解
问题定义：本文旨在解决公共服务AI治理框架在通用AI时代面临的安全性和有效性问题。现有方法依赖于狭义AI的特性，无法适应GPAI的多样性和复杂性。
核心思路：论文的核心思路是强调狭义AI与通用AI之间的明确区分，提出在治理框架中重新审视AI安全的定义和标准，以适应GPAI的特性。
技术框架：整体架构包括对现有治理框架的评估、对GPAI特性的分析，以及提出新的治理建议。主要模块包括风险评估、技术选择和政策建议。
关键创新：最重要的技术创新点在于重新定义AI安全的标准，强调GPAI的特性如何影响治理框架的有效性，与现有方法的本质区别在于不再将安全视为内在特性，而是作为可选附加功能。
关键设计：关键设计包括对治理框架中各项指标的重新审视，如准确性、偏见和可解释性，建议采用更为简约的技术方案，并强调建立国家级安全基础设施的必要性。
🖼️ 关键图片
📊 实验亮点
研究表明，现有的警务AI治理策略在面对GPAI时存在严重缺陷，尤其是在准确性和问责制方面。建议的治理框架能够有效识别这些问题，并提出相应的解决方案，确保AI的安全部署。
🎯 应用场景
该研究的潜在应用领域包括公共安全、社会服务和政府决策等。通过明确狭义与通用AI的治理框架，可以提高公共服务中AI的安全性和有效性，确保技术的负责任使用，进而提升公众信任和服务质量。
📄 摘要（原文）
Public services face growing pressure to adopt artificial intelligence (AI) to close the gap between rising demand and falling resources. That pressure has intensified with general-purpose AI (GPAI): AI built on large language models that can be directed by prompt alone to perform an effectively unbounded range of tasks. We argue that the properties that make these models attractive - their generality, accessibility, and low deployment cost - undermine the conditions under which AI safety has historically been pursued. The safety concepts that public service governance frameworks foreground - accuracy, bias, explainability, and accountability - were made tractable by narrow, purpose-built AI, and the mitigations that guidance documents prescribe presuppose exactly what GPAI removes. Accuracy cannot be quantified over unbounded outputs. Bias cannot be disaggregated when outputs are free-text judgements rather than categorical predictions. Explainability gives way to the appearance of explanation, and accountability erodes as outputs are optimized to persuade. We develop this through the case of policing, where the consequences of governance failure are most severe, and show why the same failure is likely to recur across other public services. The two mitigations that dominate policing AI strategy - expert evaluation and human-in-the-loop oversight - both rest on assumptions that GPAI violates. Safety assurance thus shifts from an intrinsic feature of building an AI tool to an optional add-on. We recommend a clear taxonomic distinction between narrow and general-purpose AI in governance documentation, a preference for technological parsimony, a pause on operational deployment of GPAI in policing until adequate evidence exists, and a coordinated national safety infrastructure with the authority to generate that evidence and determine when responsible deployment is achievable.