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.

翻译：伦理人工智能领域日益增多的研究试图通过简单的计算模型捕捉人类的道德判断。本工作的核心问题是：通过聚焦肾脏分配这一具体应用场景，探讨此类简单的人工智能模型是否能够捕捉道德决策中的关键细微差别。我们进行了二十次访谈，参与者解释了他们对肾脏应分配给谁的判断依据。我们观察到参与者：（a）对患者道德相关属性的重视程度存在差异；（b）采用多样化的决策过程，常引用启发式方法以降低决策复杂度；（c）可能改变其观点；（d）有时对其决策缺乏信心（例如，由于信息不完整）；（e）对人工智能辅助人类进行肾脏分配决策既表现出热情，也表达了担忧。基于这些发现，我们讨论了将道德判断作为人类输入的替代进行计算的建模挑战，指出了当前方法的缺陷，并提出了解决这些问题的未来研究方向。