ArticleNature communications2023
Trust within human-machine collectives depends on the perceived consensus about cooperative norms.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
11 citing papers in PubMed.
- LLM agents overcome the machine penalty when acting fairly but not when acting selfishly or altruistically.National science review · 2026Article
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- The science fiction science method.Nature · 2025Review
- Rewards and punishments help humans overcome biases against cooperation partners assumed to be machines.iScience · 2025Article
- Artificial intelligence voice gender, gender role congruity, and trust in automated vehicles.Scientific reports · 2025Article
- Reputation-based reciprocity in human-bot and human-human networks.PNAS nexus · 2025Article
- Self-powered droplet manipulation for full human-droplet interaction in multiple mediums.Nature communications · 2025Article
- Social Preferences Toward Humans and Machines: A Systematic Experiment on the Role of Machine Payoffs.Perspectives on psychological science : a journal of the Association for Psychological Science · 2025Article
- The need for an empirical research program regarding human-AI relational norms.AI and ethics · 2025Article
- Exploring Technology Acceptance of Healthcare Devices: The Moderating Role of Device Type and Generation.Sensors (Basel, Switzerland) · 2024Article
- The impact of generative artificial intelligence on socioeconomic inequalities and policy making.PNAS nexus · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.