人工智能监管中的有效性、可靠性与透明度
Validity, Reliability, and Transparency in Artificial Intelligence Regulation
浏览论文内容
中文总结 AI 辅助
本文针对现有AI监管框架未将AI推断有效性作为部署前提的不足，结合印度《普塔斯瓦米》判决的信息自决原则，提出含有效性评估、部署后监测的结构化可操作AI监管框架。
中文摘要 AI 辅助
人工智能（AI）系统日益成为影响个人与社会的决策中介。现有数据保护框架针对特定隐私相关危害，尤其是数据泄露、重识别及画像带来的危害，但未能充分捕捉一项更根本的风险：即便数据收集与处理合法，AI系统仍可能产生不可靠或缺乏正当性的推断。本文指出，现代AI引发了构念效度、混杂、代表性、分布偏移及公平性权衡等独特问题，需要专门的监管关注。在AI语境下，透明度与可解释性的含义比传统软件更为独特且挑战性显著。批判数据研究与测量理论文献中的大量研究已揭示这些认知局限，本文的贡献在于从该诊断中推导得出结构化且可操作的监管框架。本文主张，推断的有效性应作为相称性评估与部署批准的前提条件——这是包括《欧盟AI法案》基于领域的风险层级在内的现有框架未采取的举措。本文以印度最高法院《普塔斯瓦米》判决中阐明的信息自决宪法原则为基础，将其适用范围从数据收集扩展至数据使用的正当性。因此，有效的治理必须纳入AI特有的有效性评估、部署后监测，以及基于认知风险与潜在收益的结构化表述的相称性评估。
英文摘要
AI systems increasingly produce claims about people that shape access to employment, healthcare, and other consequential services. Yet lawful data processing and predictive accuracy do not establish that these claims justify the treatment that follows. We argue that regulation must therefore examine the legitimacy of the inferential step connecting data to claims and claims to decisions. This requires construct, internal, and external validity: evidence must support the meaning attributed to an output, the relationship asserted, and its application to the intended people and setting. We show why predictive performance alone cannot provide this warrant. Non-identifiability limits what observations can explain, while omissions and confounding require domain-specific judgment about what the evidence supports. Building on validity provisions in the EU AI Act and NIST AI RMF Playbook, we develop an explicit evidentiary burden for consequential reliance. Its normative basis follows from the dignity, autonomy, and informational-privacy principles in \emph{Puttaswamy} \citep{Puttaswamy2017Privacy}, which we extend from scrutiny of information acquisition to the justification of derived claims and their use. On this account, benefits that depend on an inference can carry weight in proportionality assessment only to the extent that the inference is substantiated. We make this requirement operational through a claim-specific \emph{Validity Case} linking evidence and assumptions to permitted uses, independent review, monitoring, and remedies. Hiring and LLM applications in healthcare and legal assistance illustrate how the framework can guide oversight of consequential public and private services through the relevant legal instruments.
