Moral diversity and the challenge of responsibility in AI-CDSS.

Abstract

The increasing integration of artificial intelligence in clinical decision support systems (AI-CDSS) has fueled expectations of more personalized and effective diagnostics and therapies. By incorporating machine learning methods, AI-CDSS promise enhanced predictive accuracy, improved stratification, and innovative individualized care. However, this technological optimism is accompanied by complex ethical challenges, including issues of explainability, trust, autonomy, and data security. At the core of these debates lies the question of responsibility, which involves both its attribution and diffusion, as well as the underlying normative standards guiding moral action. In the context of healthcare practice, responsibility is further complicated by moral diversity-the coexistence of varying moral values, cultural beliefs, and ethical frameworks among healthcare professionals, patients, and institutional stakeholders. This plurality challenges the establishment of a unified normative standard necessary for ethically sound responsibility attribution. This paper offers an analysis of moral diversity and AI-CDSS as a challenge for responsibility in healthcare environments. Using a relational concept of responsibility the study examines key areas in which moral diversity affects responsibility in AI-mediated decision-making. This includes algorithmic bias, healthcare professional and patient interaction and the role of patients. Through these examples, the paper explains how different normative standards intensify ethical complexity in AI-supported clinical contexts. It argues that greater ethical sensitivity to moral diversity is essential-both in the development of AI-CDSS and in their application within morally value-laden healthcare situations.