Human Artistic Discernment as an Evaluative Criterion in the Age of Artificial Intelligence

The rapid expansion of artificial intelligence has profoundly transformed contemporary practices of evaluation in academic and artistic domains. While AI systems have significantly enhanced the efficiency, scale, and consistency of assessment, they simultaneously raise fundamental questions regarding meaning, value, and responsibility dimensions that resist full automation. In this context, the present study revisits artistic discernmentas a core human capacity and examines its relevance as an evaluative criterion in the age of artificial intelligence. This paper conceptualizes artistic discernment through a dual framework. First, from a philosophical perspective, artistic discernment is understood as a distinctly human form of judgment grounded in contextual interpretation, value-based reasoning, and ethical reflection. Unlike algorithmic evaluation, which operates through pattern recognition and probabilistic inference, artistic discernment entails the capacity to situate works within broader cultural, historical, and moral contexts, thereby enabling judgments that are meaning-oriented rather than merely data-driven. Second, the study explores the methodological visibility of artistic discernment. Although traditionally regarded as subjective and intuitive, discernment is argued to exhibit recurring evaluative patterns that can be partially structured and examined through statistical approaches. Using simulated expert-rating data in the domain of music evaluation, the paper illustrates how consensus(Kendall’s W), internal consistency(Cronbach’s α), discriminative capacity(many-facet Rasch modeling), and weighting structures(AHP and entropy-based methods) can be employed to demonstrate the operational stability of discernment without reducing it to a purely quantitative construct. Importantly, this study does not propose artistic discernment as an alternative to artificial intelligence, nor does it seek to quantify human judgment exhaustively. Rather, it positions discernment as a complementary evaluative capacity that addresses dimensions of meaning, value, and ethical responsibility beyond the reach of automated systems. By articulating artistic discernment as both philosophically grounded and methodologically approachable, this paper contributes to ongoing discussions on human-centered evaluation and offers a conceptual foundation for rethinking the role of human judgment in AI-mediated assessment environments.