Trust within human-machine collectives depends on the perceived consensus about cooperative norms
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With the progress of artificial intelligence and the emergence of global online communities, humans and machines are increasingly participating in mixed collectives in which they can help or hinder each other. Human societies have had thousands of years to consolidate the social norms that promote cooperation; but mixed collectives often struggle to articulate the norms which hold when humans coexist with machines. In five studies involving 7917 individuals, we document the way people treat machines differently than humans in a stylized society of beneficiaries, helpers, punishers, and trustors. We show that a different amount of trust is gained by helpers and punishers when they follow norms over not doing so. We also demonstrate that the trust-gain of norm-followers is associated with trustors' assessment about the consensual nature of cooperative norms over helping and punishing. Lastly, we establish that, under certain conditions, informing trustors about the norm-consensus over helping tends to decrease the differential treatment of both machines and people interacting with them. These results allow us to anticipate how humans may develop cooperative norms for human-machine collectives, specifically, by relying on already extant norms in human-only groups. We also demonstrate that this evolution may be accelerated by making people aware of their emerging consensus.
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Human behavior tends to become more selfish in human-machine interactions because reciprocation-a vital factor in sustaining prosocial behavior-is not maintained as consistently as in human-human interactions.
“Human behavior tends to become more selfish in human–machine interactions because reciprocation—a vital factor in sustaining prosocial behavior—is not maintained as consistently as in human–human interactions (49, 50).”
From The impact of generative artificial intelligence on socioeconomic inequalities and policy making (2024) · cites this paper as [50]
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- Authors
- Makovi, K., Sargsyan, A., Li, W., Bonnefon, J.-F., & Rahwan, T.
- Affiliations
- New York University Abu Dhabi (AE)
- Technical University of Munich (DE)
- Centre National de la Recherche Scientifique (FR)
- Corresponding author
- Rahwan, Talal
- Venue
- Nature Communications
- Published
- 2023-05-30
- Volume
- 14
- Issue
- 1
- Pages
- 3108-3108
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC10229533
- OpenAlex
- W4378782864
- Cited by
- 36
- References
- 48
- Citations by year
- 2026: 5 · 2025: 16 · 2024: 10 · 2023: 4 · 2022: 1
- FWCI
- 10.36
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- HAL (submittedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Toulouse Capitole Publications (University Toulouse 1 Capitole) (submittedVersion)
- Funders
- New York University Abu Dhabi (Research Enhancement Funds)
- Agence Nationale de la Recherche (19-PI3A-0004, 17-EURE-0010)
- Topics
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Social Sciences › Safety Research › Experimental Behavioral Economics Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Psychology of Moral and Emotional Judgment
- Keywords
- trust in machines, cooperative norms, helping behavior, punishment, social norms
- MeSH terms
- Artificial Intelligence, Cooperative Behavior, Humans, Consensus, Trust, Social Norms
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