A Quantum Probability Approach to Improving Human-AI Decision Making.

IF 2 3区 物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY
Entropy Pub Date : 2025-02-02 DOI:10.3390/e27020152
Scott Humr, Mustafa Canan, Mustafa Demir
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Abstract

Artificial intelligence is set to incorporate additional decision space that has traditionally been the purview of humans. However, AI systems that support decision making also entail the rationalization of AI outputs by humans. Yet, incongruencies between AI and human rationalization processes may introduce uncertainties in human decision making, which require new conceptualizations to improve the predictability of these interactions. The application of quantum probability theory (QPT) to human cognition is on the ascent and warrants potential consideration to human-AI decision making to improve these outcomes. This perspective paper explores how QPT may be applied to human-AI interactions and contributes by integrating these concepts into human-in-the-loop decision making. To capture this and offer a more comprehensive conceptualization, we use human-in-the-loop constructs to explicate how recent applications of QPT can ameliorate the models of interaction by providing a novel way to capture these behaviors. Followed by a summary of the challenges posed by human-in-the-loop systems, we discuss newer theories that advance models of the cognitive system by using quantum probability formalisms. We conclude by outlining areas of promising future research in human-AI decision making in which the proposed methods may apply.

改进人类-人工智能决策的量子概率方法。
人工智能将纳入传统上属于人类范围的额外决策空间。然而,支持决策的人工智能系统也需要人类对人工智能输出进行合理化。然而,人工智能和人类合理化过程之间的不一致性可能会给人类决策带来不确定性,这需要新的概念来提高这些相互作用的可预测性。量子概率论(QPT)在人类认知中的应用正在上升,并有必要考虑人类-人工智能决策,以改善这些结果。这篇观点论文探讨了QPT如何应用于人类与人工智能的交互,并通过将这些概念集成到人在环决策中做出贡献。为了捕获这一点并提供更全面的概念化,我们使用human-in-the-loop构造来解释QPT的最新应用程序如何通过提供捕获这些行为的新方法来改进交互模型。然后总结了人在环系统所带来的挑战,我们讨论了通过使用量子概率形式化来推进认知系统模型的新理论。最后,我们概述了人类-人工智能决策中有希望的未来研究领域,其中所提出的方法可能适用。
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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来源期刊
Entropy
Entropy PHYSICS, MULTIDISCIPLINARY-
CiteScore
4.90
自引率
11.10%
发文量
1580
审稿时长
21.05 days
期刊介绍: Entropy (ISSN 1099-4300), an international and interdisciplinary journal of entropy and information studies, publishes reviews, regular research papers and short notes. Our aim is to encourage scientists to publish as much as possible their theoretical and experimental details. There is no restriction on the length of the papers. If there are computation and the experiment, the details must be provided so that the results can be reproduced.
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