规范性人工智能：一种用于在不确定性下审计人类决策的正式范式
Prescriptive Artificial Intelligence: A Formal Paradigm for Auditing Human Decisions Under Uncertainty
AI总结：
本文提出规范性人工智能作为高风险环境下的人类-人工智能决策协作范式，通过四个公理证明了其与预测系统的核心区别，并展示了在决策模仿中系统性偏差的限制。
摘要：
我们正式将规范性人工智能定义为一种用于高风险环境的人类-人工智能决策协作的独立范式。不同于优化结果准确性的预测系统，规范性系统旨在在不确定性下推荐和审计人类决策，提供规范性指导同时保持人类的自主性和问责性。我们引入了四个领域无关的公理来刻画规范性系统，并证明了基本的分离结果。其中心成果是模仿不完全定理，该定理确立了在没有外部规范信号的情况下，通过历史决策进行监督学习无法纠正系统性决策偏差。因此，决策模仿的性能受限于结构偏差项epsilon_bias，而不是统计学习率O(1/sqrt(n))。这一结果正式化了在人类决策模仿任务中经验观察到的准确性上限，并提供了一个原则性的标准来决定何时应将自动化替换为知识审计。我们通过一个可解释的模糊推理系统展示了该框架的计算可实现性，应用于精英足球决策制定的应力测试中，揭示了被结果和现状偏见掩盖的系统性决策延迟和风险状态。所提出的框架将规范性人工智能确立为一种通用、可实现的决策支持系统，适用于安全关键领域，其中可解释性、可争论性和规范性对齐是必不可少的。
英文摘要：
We formalize Prescriptive Artificial Intelligence as a distinct paradigm for human-AI decision collaboration in high-stakes, stochastic environments involving single-agent individual decision-making. Unlike predictive systems optimized for outcome accuracy, prescriptive systems audit human decisions under uncertainty, providing normative guidance while preserving human agency and accountability. We introduce four domain-independent axioms characterizing prescriptive systems and prove fundamental separation results. Central is the Imitation Incompleteness theorem: supervised learning from historical decisions cannot correct systematic biases in the absence of external normative signals. Under standard regularity conditions, the induced predictor converges almost surely to the biased action rather than the normatively optimal one. Performance in decision imitation is therefore bounded by a structural bias term (epsilon_bias) rather than the statistical rate O(1/sqrt(n)), a result extended to Markovian logs and finite-sample concentration bounds. We demonstrate realizability through three independent instantiations spanning five decades: an interpretable fuzzy system for elite soccer auditing, revealing decision latency and risk states obscured by outcome and status quo biases; MYCIN, the historically validated rule-based clinical consultation system; and NEWS2, a nationally mandated clinical protocol validated on a prospective multi-center cohort. The framework establishes Prescriptive AI as a general, realizable class of decision-support systems for safety-critical domains where interpretability, contestability, and normative alignment are essential.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
