从准确度到影响：用于对齐工程架构与影响理论的影响力驱动AI框架（IDAIF）
From Accuracy to Impact: The Impact-Driven AI Framework (IDAIF) for Aligning Engineering Architecture with Theory of Change
AI总结：
IDAIF通过整合影响理论与AI架构，提供了一种以影响为中心的AI开发框架，旨在提升AI系统的伦理性和社会价值。
摘要：
本文介绍了影响力驱动AI框架（IDAIF），一种将影响理论（ToC）原则与现代人工智能系统设计相结合的新型架构方法。随着人工智能系统日益影响高风险领域，包括医疗、金融和公共政策，对齐问题——确保人工智能行为与人类价值观和意图一致——变得至关重要。当前的方法主要优化技术性能指标，而忽视了人工智能部署的社会技术维度。IDAIF通过建立影响理论五阶段模型（输入-活动-输出-结果-影响）与相应AI架构层（数据层-流水线层-推理层-代理层-规范层）之间的系统映射来解决这一差距。每一层都包含严谨的理论基础：多目标帕累托优化用于价值对齐，分层多代理协调用于结果实现，因果有向无环图（DAGs）用于缓解幻觉，以及对抗去偏见与强化学习从人类反馈（RLHF）用于公平性保障。我们为每个组件提供了形式数学公式，并引入了管理假设失败的守护架构的保障层。三个案例研究展示了IDAIF在医疗、网络安全和软件工程领域的应用。该框架代表了从模型为中心到影响为中心的AI发展范式转变，为工程师提供了构建伦理、可信和社会有益的人工智能系统的具体架构模式。
英文摘要：
This paper introduces the Impact-Driven AI Framework (IDAIF), a novel architectural methodology that integrates Theory of Change (ToC) principles with modern artificial intelligence system design. As AI systems increasingly influence high-stakes domains including healthcare, finance, and public policy, the alignment problem--ensuring AI behavior corresponds with human values and intentions--has become critical. Current approaches predominantly optimize technical performance metrics while neglecting the sociotechnical dimensions of AI deployment. IDAIF addresses this gap by establishing a systematic mapping between ToC's five-stage model (Inputs-Activities-Outputs-Outcomes-Impact) and corresponding AI architectural layers (Data Layer-Pipeline Layer-Inference Layer-Agentic Layer-Normative Layer). Each layer incorporates rigorous theoretical foundations: multi-objective Pareto optimization for value alignment, hierarchical multi-agent orchestration for outcome achievement, causal directed acyclic graphs (DAGs) for hallucination mitigation, and adversarial debiasing with Reinforcement Learning from Human Feedback (RLHF) for fairness assurance. We provide formal mathematical formulations for each component and introduce an Assurance Layer that manages assumption failures through guardian architectures. Three case studies demonstrate IDAIF application across healthcare, cybersecurity, and software engineering domains. This framework represents a paradigm shift from model-centric to impact-centric AI development, providing engineers with concrete architectural patterns for building ethical, trustworthy, and socially beneficial AI systems.
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。
