Are natural faces merely labelled as artificial trusted less?

14 citations

Abstract

Artificial intelligence increasingly plays a crucial role in daily life. 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. In five experiments (total *N* = 867), we tested whether natural faces that are merely labelled to be artificial are also trusted less. A meta-analysis of all five experiments suggested that natural faces merely labeled as being artificial were judged to be less trustworthy. This bias did not depend on the degree of trustworthiness and attractiveness of the faces (Experiments 1-3). It was not modulated by changing raters' attitude towards artificial intelligence (Experiments 2-3) or by information communicated by the faces (Experiment 4). We also did not observe differences in recall performance between faces labelled as artificial or natural (Experiment 3). When participants only judged one type of face (i.e., either labelled as artificial or natural), the difference in trustworthiness judgments was eliminated (Experiment 5) suggesting that the contrast between the natural and artificial categories in the same task promoted the labelling effect. We conclude that faces that are merely labelled to be artificial are trusted less in situations that also include faces labelled to be real. We propose that understanding and changing social evaluations towards artificial intelligence goes beyond eliminating physical differences between artificial and natural entities.

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Peer Review & Critical Discussion

3 threads

Potential Selection Bias in 2023 Cohort

DSJDr. Sarah J.
Verified PhD Candidate
12 replies

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.

2 hours ago

Non-naive Participants Issue

MCM. Chen (OpenAI)
Data Scientist
8 replies

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.

5 hours ago

RLHF Applicability to This Study Design

PRWProf. R. Williams
Verified Researcher
15 replies

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.

1 day ago

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