Which artificial intelligences do people care about most? A conjoint experiment on moral consideration

16 citations

Abstract

Many studies have identified particular features of artificial intelligences (AI), such as their autonomy and emotion expression, that affect the extent to which they are treated as subjects of moral consideration. However, there has not yet been a comparison of the relative importance of features as is necessary to design and understand increasingly capable, multi-faceted AI systems. We conducted an online conjoint experiment in which 1,163 participants evaluated descriptions of AIs that varied on these features. All 11 features increased how morally wrong participants considered it to harm the AIs. The largest effects were from human-like physical bodies and prosociality (i.e., emotion expression, emotion recognition, cooperation, and moral judgment). For human-computer interaction designers, the importance of prosociality suggests that, because AIs are often seen as threatening, the highest levels of moral consideration may only be granted if the AI has positive intentions.

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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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