企业AI中遗留系统、治理与决策完整性的数学框架
A Mathematical Framework for Legacy, Governance, and Decision Integrity in Enterprise AI
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中文总结 AI 辅助
本文提出面向企业AI的数学框架，含遗留评分等6项核心内容、8项决策完整性规则等，通过模拟测试验证其内部行为，为企业AI决策的合规性与稳定性提供概念性支撑。
中文摘要 AI 辅助
企业人工智能正日益融入各类决策，尽管存在人员流动、模型更换、监管变化及组织激励调整等情况，这些决策仍必须保持合法、可解释、可适应且可问责。现有治理框架提供了重要原则，但本身并未提供紧凑的数学语言，以评估机构能否长期保持合理判断。本文开发了一种面向机构遗留性的设计科学框架：即决策系统在原设计者退出后，持续产生有益、合法、可解释且可适应结果的持久能力。该框架的贡献包括：（i）基于知识保留、治理、人工监督、适应性、反馈学习及司法保真度的惩罚几何均值的标准化遗留评分（Legacy Score）；（ii）将证据置信度与后果分离的决策置信度（Decision Confidence）和决策风险（Decision Risk）模型；（iii）感知权限的检索与校准弃权（不执行）；（iv）用于受治理组织学习的决策记忆（Decision Memory）；（v）将变化暴露映射至审查间隔的监管变化速度（Regulatory Change Velocity）；（vi）保留来源与法律层级的联邦监管知识图谱架构。本文还提出了八项AI决策完整性规则、评估协议及可复现的计算演示。该演示结合确定性压力测试与200次蒙特卡洛重复，每次重复包含10000个合成决策，展示了遗留评分的非补偿性，并将感知后果与权限的路由与匹配覆盖范围的仅置信度基线进行比较。该贡献仍为概念性的，未经过实地验证；模拟测试的是内部行为，而非生产性能，所有参数均需针对具体场景校准。
英文摘要
Enterprise artificial intelligence is increasingly embedded in decisions that must remain lawful, explainable, adaptable, and accountable despite personnel turnover, model replacement, regulatory change, and shifting organizational incentives. Existing governance frameworks provide important principles but do not by themselves supply a compact mathematical language for evaluating whether an institution can preserve sound judgment over time. This paper develops a design-science framework for institutional legacy: the durable capacity of a decision system to continue producing beneficial, lawful, explainable, and adaptable outcomes after its original designers have stepped away. The framework contributes: (i) a normalized Legacy Score based on a penalized geometric mean of knowledge retention, governance, human oversight, adaptability, feedback learning, and jurisdictional fidelity; (ii) Decision Confidence and Decision Risk models separating evidentiary confidence from consequence; (iii) authority-aware retrieval and calibrated abstention; (iv) Decision Memory for governed organizational learning; (v) Regulatory Change Velocity mapping change exposure to review intervals; and (vi) a federated regulatory knowledge-graph architecture preserving provenance and legal hierarchy. The paper also proposes eight AI Decision Integrity Rules, an evaluation protocol, and a reproducible computational demonstration. The demonstration combines a deterministic stress test with 200 Monte Carlo replications of 10,000 synthetic decisions each, illustrating Legacy Score non-compensation and comparing consequence- and authority-aware routing with a matched-coverage confidence-only baseline. The contribution remains conceptual rather than field-validated; the simulation tests internal behavior, not production performance, and all parameters require context-specific calibration.
发表机构
- The Math Behind Innovation（创新数学研究院）
机构由 AI 辅助整理，请以论文原文为准。
