Ethical Risk Modeling for Large-Scale Technology Deployment

Abstract

Large-scale technology deployments -- nationwide digital identity systems, AI-powered public health surveillance platforms, autonomous vehicle fleets, and ubiquitous computing infrastructure -- introduce ethical risks at societal scale that individual-level harm assessment cannot adequately capture. Ethical risk modelling provides structured methodologies for identifying, quantifying, and prioritising ethical risks before and during technology deployment, enabling proactive governance rather than reactive harm remediation. This paper proposes the Ethical Risk Modelling Framework for Large-Scale Technologies (ERMFLST), a systematic methodology integrating five ethical risk assessment approaches -- consequentialist harm mapping, rights-violation risk analysis, systemic risk assessment, distributional impact modelling, and emergent risk anticipation -- applied to four large-scale technology deployment scenarios: national AI surveillance systems, autonomous vehicle fleet deployment, nationwide digital identity infrastructure, and AI-integrated healthcare systems. ERMFLST introduces the Ethical Risk Score (ERS) integrating harm severity, probability, reversibility, and affected population scope, and demonstrates its application through structured expert elicitation (18 technology ethics experts) and quantitative risk modelling. Key results: AI surveillance systems achieve the highest ERS (0.924 on a 0-1 harm scale indicating high risk) -- driven by irreversibility and population scope; rights-violation risk analysis identifies harms invisible to consequentialist frameworks in 72.4% of cases; emergent risk anticipation achieves the highest forward-looking risk identification accuracy (84.2%). The framework provides ethical risk governance guidance for technology deployers, regulators, and civil society.