AI systems generate ethical tensions that cannot be addressed through principle-based guidance alone. This paper brings forward an Integrated Axiology–MCDA Framework for AI ethics that distinguishes intrinsic, instrumental, and relational values and uses multi-criteria analysis to operationalize value pluralism in practice. The framework structures ethical evaluation by making value commitments explicit, enabling transparent examination of trade-offs, and supporting context-sensitive judgment. A healthcare hyper-scenario with sensitivity analysis shows how different weight configurations influence the relative acceptability of diagnostic systems and clarifies the thresholds at which accuracy considerations outweigh privacy or fairness. Cross-domain applications in education, criminal justice, and finance further illustrate how domain-specific value tensions require distinct criteria sets and weighting structures. The analysis shows that ethical challenges in AI arise from genuine value pluralism. Explicit value classification enables more accountable decision making across the AI lifecycle.
Sun et al. (Mon,) studied this question.