研究人工智能能否模拟人类在肾移植分配中的复杂道德判断。
Can AI Model the Complexities of Human Moral Decision-Making? A Qualitative Study of Kidney Allocation Decisions
- 通过访谈20人，分析其肾分配决策的伦理依据和思维过程。
- 发现人类对患者属性权重不同，决策方式多样且易受信息影响。
- 揭示当前简单AI模型难以捕捉人类道德判断的深层复杂性。
越来越多的研究试图通过简单的计算模型捕捉人类的道德判断。本文聚焦于肾移植分配这一具体场景，探究此类简单模型是否能真正反映人类道德决策的核心复杂性。我们进行了20次深度访谈，让参与者解释其对谁应获得肾脏的判断理由。结果表明：（a）参与者对患者的不同道德相关属性赋予不同程度的重要性；（b）采用多种决策策略，常依赖启发式方法以降低决策复杂度；（c）观点可能随情境变化；（d）在信息不全时常常缺乏信心；（e）对人工智能辅助肾分配既表现出期待也存有担忧。基于这些发现，我们讨论了将算法作为人类输入替代品所面临的挑战，指出现有方法的局限性，并提出未来改进方向。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。