How AI can learn from the law: putting humans in the loop only on appeal.
How AI can learn from the law: putting humans in the loop only on appeal.
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DOI：
10.1038/s41746-023-00906-8
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发表时间：
2023-08-25
影响因子：
15.2
通讯作者：
中科院分区：
文献类型：
--
作者：
虽然关于在人工智能（AI）和机器学习（ML）中引入“人在回路中”的文献已经显着增长，但对人类专业知识如何与AI/ML判断相结合的关注有限。这个设计问题的出现是因为今天在公众普遍不愿意放弃人类专家判断的情况下，算法决策的普遍性和数量。为了解决这一冲突，我们建议通过上诉程序将人类专家法官包括在内，以审查算法决策。因此，人类仅在有限数量的情况下进行干预，并且仅在做出初始AI/ML判断之后。基于与司法决策中的上诉程序的类比，我们认为，在许多方面，这是一种更有效的方式来划分人类和机器之间的劳动。人类评审员可以添加更细致的临床、道德或法律的推理，他们可以考虑不容易量化的特定病例信息，因此在初始阶段AI/ML无法获得这些信息。在这样做的过程中，人类可以作为AI/ML的关键纠错检查，同时保留AI/ML在决策过程中使用的大部分效率。在本文中，我们发展了这些广泛适用的论点，同时主要关注AI/ML在医学中的应用，包括器官分配、生育护理和再入院。
While the literature on putting a “human in the loop” in artificial intelligence (AI) and machine learning (ML) has grown significantly, limited attention has been paid to how human expertise ought to be combined with AI/ML judgments. This design question arises because of the ubiquity and quantity of algorithmic decisions being made today in the face of widespread public reluctance to forgo human expert judgment. To resolve this conflict, we propose that human expert judges be included via appeals processes for review of algorithmic decisions. Thus, the human intervenes only in a limited number of cases and only after an initial AI/ML judgment has been made. Based on an analogy with appellate processes in judiciary decision-making, we argue that this is, in many respects, a more efficient way to divide the labor between a human and a machine. Human reviewers can add more nuanced clinical, moral, or legal reasoning, and they can consider case-specific information that is not easily quantified and, as such, not available to the AI/ML at an initial stage. In doing so, the human can serve as a crucial error correction check on the AI/ML, while retaining much of the efficiency of AI/ML’s use in the decision-making process. In this paper, we develop these widely applicable arguments while focusing primarily on examples from the use of AI/ML in medicine, including organ allocation, fertility care, and hospital readmission.
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影响因子：
3.1
作者：
Kragh MF;Karstoft H
通讯作者：
Karstoft H
影响因子：
5.4
作者：
Hoch, SJ;Schkade, DA
通讯作者：
Schkade, DA
影响因子：
5.4
作者：
GLAZER, R;STECKEL, JH;WINER, RS
通讯作者：
WINER, RS
影响因子：
8
作者：
Samuelson, Paul A.
通讯作者：
Samuelson, Paul A.
影响因子：
8.2
作者：
Dietvorst, Berkeley J.;Bharti, Soaham
通讯作者：
Bharti, Soaham