- Title
- In human-machine trust, humans rely on a simple averaging strategy
- Creator
- Love, Jonathon; Gronau, Quentin F.; Palmer, Gemma; Eidels, Ami; Brown, Scott D.
- Relation
- Cognitive Research: Principles and Implications Vol. 9, Issue 1, no. 58
- Publisher Link
- http://dx.doi.org/10.1186/s41235-024-00583-5
- Publisher
- SpringerOpen
- Resource Type
- journal article
- Date
- 2024
- Description
- With the growing role of artificial intelligence (AI) in our lives, attention is increasingly turning to the way that humans and AI work together. A key aspect of human–AI collaboration is how people integrate judgements or recommendations from machine agents, when they differ from their own judgements. We investigated trust in human–machine teaming using a perceptual judgement task based on the judge–advisor system. Participants (n = 89) estimated a perceptual quantity, then received a recommendation from a machine agent. The participants then made a second response which combined their first estimate and the machine’s recommendation. The degree to which participants shifted their second response in the direction of the recommendations provided a measure of their trust in the machine agent. We analysed the role of advice distance in people’s willingness to change their judgements. When a recommendation falls a long way from their initial judgement, do people come to doubt their own judgement, trusting the recommendation more, or do they doubt the machine agent, trusting the recommendation less? We found that although some participants exhibited these behaviours, the most common response was neither of these tendencies, and a simple model based on averaging accounted best for participants’ trust behaviour. We discuss implications for theories of trust, and human–machine teaming.
- Subject
- human–machine team; trust; automation; judge–advisor system
- Identifier
- http://hdl.handle.net/1959.13/1515581
- Identifier
- uon:56899
- Identifier
- ISSN:2365-7464
- Rights
- This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
- Language
- eng
- Full Text
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