为何公共服务人工智能治理框架在通用人工智能时代面临失败风险：来自警务领域的教训
Why Public Service AI Governance Frameworks Risk Failing in the Age of General-Purpose AI: Lessons from Policing
浏览论文内容
中文总结 AI 辅助
研究公共服务中通用人工智能治理框架面临的风险，以警务为例，指出其特性破坏安全条件，常用缓解措施失效，建议明确分类、技术简约、暂停部署并建立国家安全基础设施。
中文摘要 AI 辅助
公共服务面临着采用人工智能以弥合需求增长与资源减少之间差距的压力，通用人工智能（GPAI）加剧了这一压力。我们认为，使这些模型具有吸引力的特性——通用性、可及性和低部署成本——破坏了历史上追求人工智能安全的条件。公共服务治理框架强调的安全概念——准确性、偏差、可解释性和问责制——在狭义的、专门构建的人工智能中是易于处理的，而指导文件规定的缓解措施预先假定了GPAI所消除的内容。我们通过警务案例进行阐述，并表明同样的失败可能在其他公共服务中再次发生。主导警务人工智能策略的两种缓解措施——专家评估和人工参与监督——都基于GPAI所违反的假设。安全保障因此从构建人工智能工具的内在特征转变为一种可选的附加功能。我们建议在治理文件中对狭义人工智能和通用人工智能进行明确的分类区分，优先考虑技术简约性，在警务领域暂停GPAI的运营部署，直到有足够证据，并建立一个有权力生成该证据并确定何时可进行负责任部署的协调的国家安全基础设施。
英文摘要
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.
发表机构
- ESRC Vulnerability and Policing Futures Research Centre（ESRC脆弱性与警务未来研究中心）
- School of Law, University of Leeds（利兹大学法学院）
机构由 AI 辅助整理，请以论文原文为准。
