A Quantum Probability Approach to Improving Human–AI Decision Making
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...
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MDPI AG
2025-02-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/27/2/152 |
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| author | Scott Humr Mustafa Canan Mustafa Demir |
| author_facet | Scott Humr Mustafa Canan Mustafa Demir |
| author_sort | Scott Humr |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-4325a956d09f46e4b8bcc47f3fdc5d24 |
| institution | DOAJ |
| issn | 1099-4300 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj-art-4325a956d09f46e4b8bcc47f3fdc5d242025-08-20T03:12:18ZengMDPI AGEntropy1099-43002025-02-0127215210.3390/e27020152A Quantum Probability Approach to Improving Human–AI Decision MakingScott Humr0Mustafa Canan1Mustafa Demir2Department of Information Sciences, Naval Postgraduate School, Monterey, CA 93943, USADepartment of Information Sciences, Naval Postgraduate School, Monterey, CA 93943, USAApplied Cognitive Ergonomics Laboratory, Texas A&M University, College Station, TX 77843, USAArtificial 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.https://www.mdpi.com/1099-4300/27/2/152artificial intelligencedecision makingquantum decision theoryhuman-in-the-loopgenerative AI |
| spellingShingle | Scott Humr Mustafa Canan Mustafa Demir A Quantum Probability Approach to Improving Human–AI Decision Making Entropy artificial intelligence decision making quantum decision theory human-in-the-loop generative AI |
| title | A Quantum Probability Approach to Improving Human–AI Decision Making |
| title_full | A Quantum Probability Approach to Improving Human–AI Decision Making |
| title_fullStr | A Quantum Probability Approach to Improving Human–AI Decision Making |
| title_full_unstemmed | A Quantum Probability Approach to Improving Human–AI Decision Making |
| title_short | A Quantum Probability Approach to Improving Human–AI Decision Making |
| title_sort | quantum probability approach to improving human ai decision making |
| topic | artificial intelligence decision making quantum decision theory human-in-the-loop generative AI |
| url | https://www.mdpi.com/1099-4300/27/2/152 |
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