Psychological traits and appropriate reliance: Factors shaping trust in AI
Abstract
Research in AI-enabled decision support often focuses on technological factors influencing reliance on AI. However, the end-users of AI systems are individuals with diverse personalities which potentially lead to differences in collaborative human-computer interaction, resulting in harmful under- and over-reliance. The influence of psychological traits on appropriate reliance must be understood to enable development of more effective AI support addressing a diverse user base. This experimental mixed-methods study (*N* = 250) examined the impact of psychological traits on trust in and reliance on AI advice in classification tasks. Propensity to trust, affinity for technology interaction, and control beliefs in interacting with technology were identified as predictors for trust, which affect reliance. Thus, consideration must be given to the expected propensity to trust and the level of technological expertise among user groups when designing systems that aim to promote suitable degrees of trust and appropriate reliance.
Study specs
- Institution
- Universitätsklinikum Giessen und Marburg
- Year
- 2022
- Human Data Platform
- Prolific
- Source
- View Source DOI Google Scholar
Peer Review & Critical Discussion
Potential Selection Bias in 2023 Cohort
The participant pool shows a concerning overrepresentation of users from high-income demographics. Looking at Table 3, we can see that 78% of respondents had annual incomes above $75k, which significantly limits the generalizability of these findings to broader populations.
Non-naive Participants Issue
I've noticed a methodological concern regarding participant naivety. Given that Prolific users often complete multiple studies, there's a real risk that participants had prior exposure to similar experimental paradigms, which could confound the results.
RLHF Applicability to This Study Design
The implications for RLHF training pipelines are understated. If we accept the authors' conclusions about preference stability, this has direct consequences for how we should structure reward model training. The temporal decay effect described in Section 4.2 is particularly relevant.
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