A Quantum Model of Trust Calibration in Human-AI Interactions
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Description
This exploratory study investigates a human agent’s evolving judgements of reliability when interacting with an AI system. Two aims drove this investigation: (1) compare the predictive performance of quantum vs. Markov random walk models regarding human reliability judgements of an AI system and (2) identify a neural correlate of the perturbation of a human agent’s judgement of the AI’s reliability. As AI becomes more prevalent, it is important to understand how humans trust these technologies and how trust evolves when interacting with them. A mixed-methods experiment was developed for exploring reliability calibration in human–AI interactions. The behavioural data collected were used as a baseline to assess the predictive performance of the quantum and Markov models. We found the quantum model to better predict the evolving reliability ratings than the Markov model. This may be due to the quantum model being more amenable to represent the sometimes pronounced within-subject variability of reliability ratings. Additionally, a clear event-related potential response was found in the electroencephalographic (EEG) data, which is attributed to the expectations of reliability being perturbed. The identification of a trust-related EEG-based measure opens the door to explore how it could be used to adapt the parameters of the quantum model in real time.
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ID Code: | 250488 | ||||||||||
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Item Type: | Contribution to Journal (Journal Article) | ||||||||||
Refereed: | Yes | ||||||||||
ORCID iD: |
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Measurements or Duration: | 18 pages | ||||||||||
Keywords: | artificial intelligence, cognitive neuroscience, probabilistic models, quantum cognition, trust | ||||||||||
DOI: | 10.3390/e25091362 | ||||||||||
ISSN: | 1099-4300 | ||||||||||
Pure ID: | 172883428 | ||||||||||
Divisions: | Current > QUT Faculties and Divisions > Faculty of Science Current > Schools > School of Information Systems Current > QUT Faculties and Divisions > Faculty of Health Current > Schools > School of Exercise & Nutrition Sciences |
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Funding Information: | This material is based upon work supported by the Air Force Office of Scientific Research under award numbers: FA9550-22-1-0005, FA9550-23-1-0258. | ||||||||||
Copyright Owner: | 2023 The Authors | ||||||||||
Copyright Statement: | This work is covered by copyright. Unless the document is being made available under a Creative Commons Licence, you must assume that re-use is limited to personal use and that permission from the copyright owner must be obtained for all other uses. If the document is available under a Creative Commons License (or other specified license) then refer to the Licence for details of permitted re-use. It is a condition of access that users recognise and abide by the legal requirements associated with these rights. If you believe that this work infringes copyright please provide details by email to qut.copyright@qut.edu.au | ||||||||||
Deposited On: | 09 Jul 2024 03:28 | ||||||||||
Last Modified: | 21 Jul 2024 22:21 |
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