Faces merely labelled as artificial are trusted less
Abstract
Artificial intelligence plays a crucial role on our daily lives. At the same time, artificial intelligence is often met with reluctance and distrust. Previous research demonstrated that faces that are visibly artificial are considered to be less trustworthy and remembered less accurately compared to natural faces. Current technology, however, enables the generation of artificial faces that are indistinguishable from natural faces. Accordingly, we tested whether natural faces that are merely labelled to be artificial are also trusted less. In three experiments (N = 399), we observed that natural faces merely labeled as being artificial were judged to be less trustworthy. This bias was robust and did not depend on the degree of trustworthiness and attractiveness of the faces, nor could it be modulated by changing raters' attitude towards artificial intelligence. At the same time, we did not observe differences in recall performance. We conclude that understanding and changing social evaluations towards artificial intelligence goes beyond eliminating physical differences between artificial and natural entities.
Study specs
- Authors
- B Liefooghe,M Oliveira,LM Leisten,E Hoogers
- Institution
- Utrecht University
- Discipline
- Artificial Intelligence,Psychology
- 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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