Algorithmic Decision-Making in Difficult Scenarios

Christopher B. Rauch, Ursula Addison, Michael Floyd, Prateek Goel, Justin Karneeb, Ray Kulhanek, O. Larue, David Ménager, Mallika Mainali, Matthew Molineaux, Adam Pease, Anik Sen, Jt Turner, Rosina Weber
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Abstract

We present an approach to algorithmic decision-making that emulates key facets of human decision-making, particularly in scenarios marked by expert disagreement and ambiguity. Our system employs a case-based reasoning framework, integrating learned experiences, contextual factors, probabilistic reasoning, domain-specific knowledge, and the personal traits of decision-makers. A primary aim of the system is to articulate algorithmic decision-making as a human-comprehensible reasoning process, complete with justifications for selected actions.
困难情况下的算法决策
我们提出了一种算法决策方法,它可以模拟人类决策的关键方面,尤其是在专家意见不一和模棱两可的情况下。我们的系统采用基于案例的推理框架,整合了所学经验、背景因素、概率推理、特定领域知识以及决策者的个人特征。该系统的主要目的是将算法决策表述为人类可理解的推理过程,并为选定的行动提供完整的理由。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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