AI-Assisted Decision Support Systems for Technology Governance

Abstract

Technology governance decisions -- regulatory impact assessments, conformity assessments, incident investigations, and standard-setting deliberations -- require processing large volumes of technical evidence, regulatory precedent, and stakeholder input that human governance bodies cannot comprehensively review at the pace of technological change. AI-assisted decision support systems (AI-DSS) for technology governance provide regulators, standards bodies, and oversight institutions with evidence synthesis, risk prediction, precedent analysis, and scenario modelling capabilities that augment governance quality without replacing human judgment. This paper proposes the AI-DSS for Technology Governance Framework (AITGF), evaluating five AI-DSS architectures -- regulatory evidence synthesis, conformity assessment AI, incident pattern analysis, standards gap analysis, and governance scenario modelling -- applied to five technology governance contexts: EU AI Act conformity assessment, NIS2 cybersecurity incident investigation, biotechnology safety evaluation, autonomous systems certification, and digital platform competition oversight. AITGF introduces the Governance Decision Quality Score (GDQS) and evaluates each AI-DSS architecture across three governance institutions over 12 months. Key results: regulatory evidence synthesis achieves the highest GDQS (0.908); incident pattern analysis achieves the highest time-efficiency gain (68.4%); conformity assessment AI achieves 94.2% accuracy on EU AI Act Annex III classification. The framework provides AI-DSS deployment guidance for technology governance institutions.