Humane Artificial Intelligence: Psychological, Social, and Ethical Dimensions
Abstract
The rapid integration of AI into various facets of daily life—ranging from healthcare and education to transportation and personal digital assistants—has ushered in transformative possibilities. However, this proliferation also brings forth complex psychological, social, and ethical challenges. As AI systems increasingly influence human behavior and decision-making, there is a pressing need to ensure that these technologies are designed and deployed in ways that prioritize human values and well-being.
Study specs
The paper conducts an interdisciplinary review of existing research and literature to analyze the psychological, social, and ethical dimensions of AI deployment.
- Authors
- G Riva,BK Wiederhold,P Cipresso
- Institution
- Università Cattolica del Sacro Cuore,University of Genova,Università degli Studi di Milano,Università di Catania
- Discipline
- AI Ethics,Psychology,Sociology
- Study Type
- Literature Review
- Year
- 2025
- Human Data Platform
- Prolific
- Source
- View Source DOI Google Scholar
Measured Outcomes
The impact of AI on human behavior, decision-making, and societal values.
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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