Evidence map›Paper›PMID 37761661›Full record

ArticleEntropy (Basel, Switzerland)2023

A Quantum Model of Trust Calibration in Human-AI Interactions.

Luisa Roeder, Pamela Hoyte, Johan van der Meer, Lauren Fell, Patrick Johnston, Graham Kerr, Peter Bruza

Abstract read
In one paragraph

Article in Entropy (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Luisa RoederSchool of Information Systems, Queensland University of Technology, Brisbane 4000, Australia.ORCID 0000-0001-8828-894X
Pamela HoyteSchool of Information Systems, Queensland University of Technology, Brisbane 4000, Australia.ORCID 0000-0002-5021-7000
Johan van der MeerSchool of Information Systems, Queensland University of Technology, Brisbane 4000, Australia.ORCID 0000-0003-2737-2691
Lauren FellSchool of Information Systems, Queensland University of Technology, Brisbane 4000, Australia.
Patrick JohnstonSchool of Exercise and Nutrition Sciences, Queensland University of Technology, Brisbane 4000, Australia.
Graham KerrSchool of Exercise and Nutrition Sciences, Queensland University of Technology, Brisbane 4000, Australia.
Peter BruzaSchool of Information Systems, Queensland University of Technology, Brisbane 4000, Australia.ORCID 0000-0001-7999-0534

Funding

United States Air Force Office of Scientific Research FA9550-22-1-0005United States Air Force Office of Scientific Research FA9550-23-1-0258
6 · The paper itself

Abstract

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.

Indexed as

artificial intelligencecognitive neuroscienceprobabilistic modelsquantum cognitiontrust

Identifiers

PMID37761661
PMCPMC10528121

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.