ReviewNature human behaviour2021
Bad machines corrupt good morals.
Review in Nature human behaviour, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
35 citing papers in PubMed.
- How large language models judge and influence human cooperation.PNAS nexus · 2026Article
- People have different expectations for their own versus others' use of AI-mediated communication tools.British journal of psychology (London, England : 1953) · 2026Article
- LLM agents overcome the machine penalty when acting fairly but not when acting selfishly or altruistically.National science review · 2026Article
- Biased AI writing assistants shift users' attitudes on societal issues.Science advances · 2026Article
- AI outperforms humans in establishing interpersonal closeness in emotionally engaging interactions, but only when labelled as human.Communications psychology · 2026Article
- Does a Smarter ChatGPT Become More Utilitarian?Science and engineering ethics · 2025Article
- Writing with AI boosts trust-building efficiency.iScience · 2025Article
- Article
- Article
- Permissibility, Moral Emotions, and Perceived Moral Agency in Autonomous Driving Dilemmas: An Investigation of Pedestrian-Sacrifice and Driver-Sacrifice Scenarios in the Third-Person Perspective.Behavioral sciences (Basel, Switzerland) · 2025Article
- The evolutionary advantage of guilt: co-evolution of social and non-social guilt in structured populations.Journal of the Royal Society, Interface · 2025Article
- Influence of AI behavior on human moral decisions, agency, and responsibility.Scientific reports · 2025Article
- Understanding the Impact of Algorithmic Discrimination on Unethical Consumer Behavior.Behavioral sciences (Basel, Switzerland) · 2025Article
- Experiences of healthcare professionals providing palliative care in home settings - a scoping review.BMC palliative care · 2025Article
- Safety of human-AI cooperative decision-making within intensive care: A physical simulation study.PLOS digital health · 2025Article
- From self-interest to collective action: The role of defaults in governing common resources.PloS one · 2025Article
- Characteristics of ChatGPT users from Germany: Implications for the digital divide from web tracking data.PloS one · 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
- A new sociology of humans and machines.Nature human behaviour · 2024Review
- Risks and protective measures for synthetic relationships.Nature human behaviour · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As machines powered by artificial intelligence (AI) influence humans' behaviour in ways that are both like and unlike the ways humans influence each other, worry emerges about the corrupting power of AI agents. To estimate the empirical validity of these fears, we review the available evidence from behavioural science, human-computer interaction and AI research. We propose four main social roles through which both humans and machines can influence ethical behaviour. These are: role model, advisor, partner and delegate. When AI agents become influencers (role models or advisors), their corrupting power may not exceed the corrupting power of humans (yet). However, AI agents acting as enablers of unethical behaviour (partners or delegates) have many characteristics that may let people reap unethical benefits while feeling good about themselves, a potentially perilous interaction. On the basis of these insights, we outline a research agenda to gain behavioural insights for better AI oversight.
Indexed as
Identifiers
34083752What OpenQuestion holds
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.