A New Framework Connects AI Architecture Directly to Societal Impact

A new study introduces an impact-driven AI framework designed to develop AI systems from the ground up based on the real societal impact they aim to achieve. The method is called the Impact-Driven AI Framework (IDAIF).

The background is the so-called alignment problem of AI: how to ensure that the operation of AI systems aligns with human values and goals, especially in high-risk areas like healthcare, finance, and public decision-making. Current AI systems are often optimized only for technical metrics such as accuracy or speed, even though the real consequences of these systems become apparent only when they are used in society.

IDAIF combines the principles of the Theory of Change, familiar from the development world, with AI architecture. The Theory of Change breaks down a project into a chain of five phases: inputs, activities, outputs, outcomes, and impact. In the study, these phases are directly linked to the layers of the AI system: the data layer, processing pipeline, inference layer, functional or "agent-like" layer, and the normative layer, which considers values and rules.

The goal is to create a systematic map that connects technical solutions and societal goals at the same design table. This way, designers could evaluate how data, algorithms, and the operational mode of AI support desired changes and what unintended consequences should be anticipated while building the system's architecture.

The study does not offer a single new algorithm but a way to structure the entire AI system so that technical performance and social consequences are considered as a whole.

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