How do citizens perceive the use of Artificial Intelligence in public sector decisions?
Abstract
Artificial Intelligence (AI) has become increasingly prevalent in almost every aspect of our lives. At the same time, a debate about its applications, safety, and privacy is raging. In three studies, we explored how UK respondents perceive the usage of AI in various public sector decisions. Our results are fourfold. First, we found that people prefer AI to have considerably less decisional weight than various human decision-makers; those being: politicians, citizens, and (human) experts. Secondly, our findings revealed that people prefer AI to provide input and advice to these human decision-makers, rather than letting AI make decisions by itself. Thirdly, although AI is seen as contributing less to perceived legitimacy than these human decision-makers, similar to (human) experts, its contribution is seen more in terms of output legitimacy than in terms of input and throughput legitimacy. Finally, our results suggest that the involvement of AI is perceived more suitable for decisions that are low (instead of high) ideologically-charged. Overall, our findings thus show that people are rather skeptical towards using AI in the public domain, but this does not imply that they want to exclude AI entirely from the decision-making process.
Study specs
Three studies surveying UK respondents on their perceptions of AI involvement in public sector decision-making.
- Institution
- Ghent University,KU Leuven
- Discipline
- Political Science,Public Administration
- Study Type
- Experimental Study
- Year
- 2024
- Human Data Platform
- Prolific
- Source
- View Source DOI Google Scholar
Measured Outcomes
Perception of AI's role in decision-making, its legitimacy compared to human decision-makers, and suitability for various types of decisions.
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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