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. 2025 Feb 2;27(2):152.
doi: 10.3390/e27020152.

A Quantum Probability Approach to Improving Human-AI Decision Making

Scott Humr  1 , Mustafa Canan  1 , Mustafa Demir  2
Affiliations

A Quantum Probability Approach to Improving Human-AI Decision Making

Scott Humr et al. Entropy (Basel). .

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.

Keywords: artificial intelligence; decision making; generative AI; human-in-the-loop; quantum decision theory.

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Conflict of interest statement

The authors declare no conflict of interest. The views expressed in this document are those of the authors and do not reflect any official policy or position of the U.S. Department of Defense or the U.S. Government.

Figures

Figure 1
Figure 1
Google Generative AI experimental search engine output for a query on Lyme disease asked on 5 July 2024. Screenshot from Google Labs AI Generative AI experiment search result demonstrated through a number of informational cues, such as highlighting text, minimization of important text using a smaller font, and a list of conditions that a user can screen themselves against and can influence user behavior or increase their uncertainty, which may increase anxiety. Results are for informational purposes only.
Figure 2
Figure 2
Lyme disease probability in the mind of Lisa over time. Temporal oscillation of probability of Lyme positive belief for Lisa; the initial probability of Lisa’s Lyme positive belief is 0.69. See Appendix A for additional explanation.
See this image and copyright information in PMC

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