Computational Methods for Measuring Social Impact of Responsible AI

Abstract

The social impact of AI systems -- the aggregate effect of AI-assisted decisions on the distribution of social opportunities, life outcomes, and fundamental rights across affected populations -- is a central concern of responsible AI governance yet remains poorly operationalised as a measurable, quantitative concept. Existing responsible AI evaluation instruments measure individual-level ethical properties but do not systematically quantify population-level and longitudinal effects of AI deployment on social equity and wellbeing. This paper proposes the Social Impact Measurement Framework for AI (SIMFA), a computational methodology for quantifying the social impact of AI systems across four impact dimensions: distributional equity, opportunity access, outcome trajectory effects, and systemic amplification risk. SIMFA comprises six computational methods drawing on causal inference, longitudinal panel analysis, and systems simulation, each specified with a formal estimand, identification strategy, data requirements, and interpretation guide. The framework is validated through retrospective application to three historical AI deployments with known social impact outcomes, and applied prospectively to three current AI systems to generate Social Impact Assessment (SIA) reports. Retrospective validation demonstrates strong predictive validity: SIMFA impact scores are strongly correlated with independently measured social outcome changes (r = 0.81, p < 0.001). Prospective assessments reveal substantial social impact risk in all three current systems, with systemic amplification risk scores exceeding the critical threshold in two of three systems. The study contributes the SIMFA specification, a validated Social Impact Index (SII), and a Social Impact Assessment reporting standard for responsible AI deployment.