人工智能算法中的偏好:不确定情况下的自动决策需要相关的风险态度。

IF 3 3区 医学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Risk Analysis Pub Date : 2024-10-01 Epub Date: 2024-01-06 DOI:10.1111/risa.14268
Elisabeth Paté-Cornell
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引用次数: 0

摘要

人工智能(AI)通过提供嵌入在大型数据库中的大量信息,以及在不确定情况下建议或执行自动决策,具有改善生活和降低风险的潜力。然而,在设计规定性人工智能算法时,如果人工智能信息有误或不完整,首先可能会出现一些问题。但本文的主要观点是,在不确定情况下,决策算法无论是否理性,除了确定性偏好之外,还以某种方式包含了风险态度。在软件中实施的风险态度是由分析人员、他们所服务的组织、为他们提供信息的专家,以及更普遍的由确定可能选项的过程所选择的。问题是,它可能代表实际决策者(风险管理者)的偏好,也可能不代表受其决策影响的人的偏好。本文简要介绍了使用人工智能输出结果的风险管理者的偏好与人工智能系统中嵌入的风险态度之间有时存在的严重差异问题。我们的建议是尽可能使这些人工智能因素具有可访问性和透明度,并在必要时允许在模型中对偏好进行调整。本文描述了两个简化示例的制定过程,一个是医生和他/她的病人使用人工智能系统决定治疗方案,另一个是帆船比赛(如美洲杯帆船赛)的船长接收人工智能处理的传感器信号,了解不同可能赛道的航行条件。
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Preferences in AI algorithms: The need for relevant risk attitudes in automated decisions under uncertainties.

Artificial intelligence (AI) has the potential to improve life and reduce risks by providing large amounts of information embedded in big databases and by suggesting or implementing automated decisions under uncertainties. Yet, in the design of a prescriptive AI algorithm, some problems may occur, first and clearly, if the AI information is wrong or incomplete. But the main point of this article is that under uncertainties, the decision algorithm, rational or not, includes, in one way or another, a risk attitude in addition to deterministic preferences. That risk attitude implemented in the software is chosen by the analysts, the organization that they serve, the experts who inform them, and more generally by the process of identifying possible options. The problem is that it may or may not represent, as it should, the preferences of the actual decision maker (the risk manager) and of the people subjected to his/her decisions. This article briefly describes the sometimes-serious problem of that discrepancy between the preferences of the risk managers who use an AI output, and the risk attitude embedded in the AI system. The recommendation is to make these AI factors as accessible and transparent as possible and to allow for preference adjustments in the model if needed. The formulation of two simplified examples is described, that of a medical doctor and his/her patient when using an AI system to decide of a treatment option, and that of a skipper in a sailing race such as the America's Cup, receiving AI-processed sensor signals about the sailing conditions on different possible courses.

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来源期刊
Risk Analysis
Risk Analysis 数学-数学跨学科应用
CiteScore
7.50
自引率
10.50%
发文量
183
审稿时长
4.2 months
期刊介绍: Published on behalf of the Society for Risk Analysis, Risk Analysis is ranked among the top 10 journals in the ISI Journal Citation Reports under the social sciences, mathematical methods category, and provides a focal point for new developments in the field of risk analysis. This international peer-reviewed journal is committed to publishing critical empirical research and commentaries dealing with risk issues. The topics covered include: • Human health and safety risks • Microbial risks • Engineering • Mathematical modeling • Risk characterization • Risk communication • Risk management and decision-making • Risk perception, acceptability, and ethics • Laws and regulatory policy • Ecological risks.
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