Ethics-Driven Evaluation Metrics for Emerging Technology Systems

Abstract

Standard technology evaluation metrics -- accuracy, throughput, latency, cost-efficiency -- optimise for technical performance while leaving ethical performance unmeasured and therefore ungoverned. Ethics-driven evaluation metrics supplement technical metrics with systematic measurement of ethical performance dimensions: fairness, transparency, human oversight quality, privacy protection, societal impact, and value alignment. This paper proposes the Ethics-Driven Evaluation Metrics Framework (EDEMF), a systematic methodology for designing, operationalising, and integrating ethics metrics into technology evaluation processes for five emerging technology system types: large language models, autonomous decision systems, AI surveillance systems, recommendation algorithms, and AI-assisted medical systems. EDEMF introduces the Ethics Metric Quality Score (EMQS) assessing metric validity, sensitivity, and governance utility across 42 candidate ethics metrics. Key results: 18 metrics achieve high EMQS (> 0.800) and are recommended as core evaluation requirements; fairness metrics show the highest variance across system types (not all fairness metrics apply to all systems); transparency metrics have the lowest measurement validity (hardest to operationalise objectively); the recommended 18-metric evaluation suite achieves comprehensive ethical coverage at manageable evaluation cost. The framework provides practical ethics metric guidance for AI developers, conformity assessors, and regulators.