From Accuracy to Impact: The Impact-Driven AI Framework (IDAIF) for Aligning Engineering Architecture with Theory of Change

• URL: http://arxiv.org/abs/2512.08449v1
• Date: Tue, 09 Dec 2025 10:21:02 GMT
• Title: From Accuracy to Impact: The Impact-Driven AI Framework (IDAIF) for Aligning Engineering Architecture with Theory of Change
• Authors: Yong-Woon Kim,
• Abstract summary: Impact-Driven AI Framework (IDAIF) is a novel architectural methodology that integrates Theory of Change (ToC) principles with modern artificial intelligence system design.<n>IDAIF addresses the gap by establishing a systematic mapping between ToC's five-stage model and corresponding AI architectural layers.<n>Three case studies demonstrate IDAIF application across healthcare, cybersecurity, and software engineering domains.
• Score: 0.40611352512781873
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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