Can AI Model the Complexities of Human Moral Decision-Making? A Qualitative Study of Kidney Allocation Decisions

• URL: http://arxiv.org/abs/2503.00940v1
• Date: Sun, 02 Mar 2025 15:42:17 GMT
• Title: Can AI Model the Complexities of Human Moral Decision-Making? A Qualitative Study of Kidney Allocation Decisions
• Authors: Vijay Keswani, Vincent Conitzer, Walter Sinnott-Armstrong, Breanna K. Nguyen, Hoda Heidari, Jana Schaich Borg,
• Abstract summary: A growing body of work in Ethical AI attempts to capture human moral judgments through simple computational models.<n>We conducted twenty interviews where participants explained their rationale for their judgments about who should receive a kidney.<n>We observe participants: (a) value patients' morally-relevant attributes to different degrees; (b) use diverse decision-making processes, citings to reduce decision complexity; (c) can change their opinions; and (e) express enthusiasm and concern regarding AI assisting humans in kidney allocation decisions.
• Score: 31.11473190744529
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A growing body of work in Ethical AI attempts to capture human moral judgments through simple computational models. The key question we address in this work is whether such simple AI models capture {the critical} nuances of moral decision-making by focusing on the use case of kidney allocation. We conducted twenty interviews where participants explained their rationale for their judgments about who should receive a kidney. We observe participants: (a) value patients' morally-relevant attributes to different degrees; (b) use diverse decision-making processes, citing heuristics to reduce decision complexity; (c) can change their opinions; (d) sometimes lack confidence in their decisions (e.g., due to incomplete information); and (e) express enthusiasm and concern regarding AI assisting humans in kidney allocation decisions. Based on these findings, we discuss challenges of computationally modeling moral judgments {as a stand-in for human input}, highlight drawbacks of current approaches, and suggest future directions to address these issues.

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