Algorithm exploitation: Humans are keen to exploit benevolent AI
Open accessAbstract
We cooperate with other people despite the risk of being exploited or hurt. If future artificial intelligence (AI) systems are benevolent and cooperative toward us, what will we do in return? Here we show that our cooperative dispositions are weaker when we interact with AI. In nine experiments, humans interacted with either another human or an AI agent in four classic social dilemma economic games and a newly designed game of Reciprocity that we introduce here. Contrary to the hypothesis that people mistrust algorithms, participants trusted their AI partners to be as cooperative as humans. However, they did not return AI's benevolence as much and exploited the AI more than humans. These findings warn that future self-driving cars or co-working robots, whose success depends on humans' returning their cooperativeness, run the risk of being exploited. This vulnerability calls not just for smarter machines but also better human-centered policies.
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Karpus et al. [61] found that while users of automated vehicles are well aware of traffic rules and the role of automated vehicles on the road, drivers are keen on exploiting automated cars as compared to cars driven by human drivers.
“Karpus et al. [61] have found that while users of automated vehicles are well aware of traffic rules and the role of automated vehicles on the road, drivers are keen on exploiting automated cars as compared to cars driven by human drivers.”
From The AI Ghostwriter Effect: When Users do not Perceive Ownership of AI-Generated Text but Self-Declare as Authors (2024) · cites this paper as Karpus et al. [61]
The Introduction proposes that people may exploit a cooperative AI agent more than a human, and the Discussion treats greater exploitation of cooperative artificial agents as a finding. But the paper uses driving as an illustration, not as the setting of its experiments; it does not establish that drivers exploit automated cars on the road or that automated-vehicle users are aware of traffic rules and the vehicles’ role. (LLM, conf=0.88)
Experimental — accuracy not yet measuredStudies have shown that people are more inclined to exploit algorithms or AI models than humans.
“Studies have shown that people are more inclined to exploit algorithms or AI models than humans [61].”
From The AI Ghostwriter Effect: When Users do not Perceive Ownership of AI-Generated Text but Self-Declare as Authors (2024) · cites this paper as [61]
The human ghostwriter was credited more often than the AI ghostwriter (H5).
“In line with the tendency of humans to more readily exploit AI than a human [61], the human ghostwriter was also credited more often than the AI ghostwriter (H5).”
From The AI Ghostwriter Effect: When Users do not Perceive Ownership of AI-Generated Text but Self-Declare as Authors (2024) · cites this paper as [61]
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- Authors
- Karpus, J., Krüger, A., Verba, J. T., Bahrami, B., & Deroy, O.
- Affiliations
- Ludwig-Maximilians-Universität München (DE)
- University of London (GB)
- Corresponding author
- Deroy, Ophelia
- Venue
- iScience
- Publisher
- Elsevier BV
- Published
- 2021-06
- Volume
- 24
- Issue
- 6
- Pages
- 102679
- ISSN
- 2589-0042 (ISSN-L)
- PMC
- PMC8219775
- OpenAlex
- W3170088386
- Cited by
- 76
- References
- 56
- Citations by year
- 2026: 20 · 2025: 27 · 2024: 13 · 2023: 10 · 2022: 4 · 2021: 2
- FWCI
- 9.07
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- iScience (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PhilPapers (PhilPapers Foundation) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Open access LMU (Ludwid Maxmilian's Universitat Munchen) (submittedVersion, cc-by-nc-nd)
- Funders
- Ludwig-Maximilians-Universität München
- European Research Council
- Horizon 2020 (819040)
- Bundesministerium für Bildung und Forschung
- NOMIS Stiftung
- Alexander von Humboldt-Stiftung
- Norges Forskningsråd
- Topics
- Social Sciences › Safety Research › Experimental Behavioral Economics Studies
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Social Sciences › Sociology and Political Science › Evolutionary Game Theory and Cooperation
- Keywords
- human-AI interaction, reciprocity, social dilemmas, cooperation, exploitation, trust in algorithms, economic games, autonomous vehicles
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