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Machine behaviour
Open accessAbstract
Machines powered by artificial intelligence increasingly mediate our social, cultural, economic and political interactions. Understanding the behaviour of artificial intelligence systems is essential to our ability to control their actions, reap their benefits and minimize their harms. Here we argue that this necessitates a broad scientific research agenda to study machine behaviour that incorporates and expands upon the discipline of computer science and includes insights from across the sciences. We first outline a set of questions that are fundamental to this emerging field and then explore the technical, legal and institutional constraints on the study of machine behaviour.
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“Such endogenous coordination failures blur the line between attack and accident (Leibo et al., 2017;Rahwan et al., 2019), reinforcing that defenses must account for both malicious swarms and emergent collective behaviors.”
From Open Challenges in Multi-Agent Security: Towards Secure Systems of Interacting AI Agents (2025) · cites this paper as Rahwan et al., 2019
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“Augmentation is therefore a coevolutionary process during which humans learn from machines and machines learn from humans (Amershi, Cakmak, Knox, & Kulesza, 2014;Rahwan et al., 2019).”
From Artificial Intelligence and Management: The Automation–Augmentation Paradox (2021) · cites this paper as Rahwan et al., 2019
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“Only such studies can examine the feedback loops between human influence on machine behavior and machine influence on human behavior, which cause the emergent behaviors that are otherwise impossible to predict (Rahwan et al., 2019).”
From Artificial Intelligence and Management: The Automation–Augmentation Paradox (2021) · cites this paper as Rahwan et al., 2019
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“Computer scientists may be expert technologists, but they are generally not trained social or behavioral scientists (Rahwan et al., 2019).”
From Artificial Intelligence and Management: The Automation–Augmentation Paradox (2021) · cites this paper as Rahwan et al., 2019
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“These differences lead to entirely new machine behaviors (Rahwan et al., 2019).”
From Artificial Intelligence and Management: The Automation–Augmentation Paradox (2021) · cites this paper as Rahwan et al., 2019
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“Openly available, robust natural language generation (NLG) algorithms have an ability to generate human-like texts across multiple domains”
From Value attributed to text-based archives generated by artificial intelligence (2023) · cites this paper as [1]
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“This means that research into the beliefs and cognitive processes underlying LLMs need to resort to more indirect and interdisciplinary methods (Rahwan et al., 2019).”
From Exploring the psychology of LLMs’ moral and legal reasoning (2024) · cites this paper as Rahwan et al., 2019
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“First, we demonstrate the effectiveness of experimental economics methods in studying choice behavior of artificial intelligence (43), which adds earlier studies of children (23, 30), monkeys (31), rats, and pigeons (32).”
From The emergence of economic rationality of GPT (2023) · cites this paper as [43]
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“orders of magnitude more accurate than some traditional technologies (Avati et al., 2017; Rahwan et al., 2019; Starke & Baber, 2020).”
MethodInfluentialProblematic Machine Behavior: A Systematic Literature Review of Algorithm Audits
Bandy, J. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3449148 · Cited by 193
“In their piece describing the study of "machine behavior," Rahwan et al. [131] explicitly suggest price discrimination as a research area.”
BackgroundSensitiveNets: Learning Agnostic Representations with Application to Face Images
Morales, A., Fiérrez, J., Vera-Rodríguez, R., & Tolosana, R. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3015420 · Cited by 185
“Algorithms might intentionally or unintentionally exploit this information with undesirable discriminatory effects [1].”
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Kohoutová, L., Heo, J., Cha, S., Lee, S., Moon, T., Wager, T. D., & Woo, C. (2020) · Nature Protocols · doi:10.1038/s41596-019-0289-5 · Cited by 176
Robot Betrayal: a guide to the ethics of robotic deception
Danaher, J. (2020) · Ethics and Information Technology · doi:10.1007/s10676-019-09520-3 · Cited by 175
Zerfaß, A., Hagelstein, J., & Tench, R. (2020) · Journal of Communication Management · doi:10.1108/jcom-10-2019-0137 · Cited by 175
“As mentioned, knowledge representation can be seen as a subset of machine learning, whereas actions of software-driven agents might be based on their situational surrounding or their past experiences (Rahwan et al. , 2019, p. 483).”
BackgroundHerrmann, T., & Pfeiffer, S. (2023) · AI & SOCIETY · doi:10.1007/s00146-022-01391-5 · Cited by 173
“In engineering and computer science, the issue of machine behavior is commonly understood as an interdisciplinary task that raises ethical questions—appeals to scholars of law, humanities, and the social sciences to weigh in are common (Rahwan et al. 2019).”
BackgroundHuman-AI agency in the age of generative AI
Krakowski, S. M. (2025) · Information and Organization · doi:10.1016/j.infoandorg.2025.100560 · Cited by 223
Gender Bias in Machine Translation
Savoldi, B., Gaido, M., Bentivogli, L., Negri, M., & Turchi, M. (2021) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00401 · Cited by 229
The emergence of economic rationality of GPT
Chen, Y., Liu, T. X., Shan, Y., & Zhong, S. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2316205120 · Cited by 164
“Our work aligns with recent calls to study machine behavior to “reap their benefits and minimize their harms” (Rahwan et al., 2019).”
BackgroundMethodResultFooled twice: People cannot detect deepfakes but think they can
Köbis, N. C., Doležalová, B., & Soraperra, I. (2021) · iScience · doi:10.1016/j.isci.2021.103364 · Cited by 193
“Overall, we find lower detection accuracy than research on static images (Groh et al., 2021; Rössler et al., 2019).”
Performance vs. competence in human–machine comparisons
Firestone, C. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1905334117 · Cited by 150
Who Gets Credit for AI-Generated Art?
Epstein, Z., Levine, S., Rand, D. G., & Rahwan, I. (2020) · iScience · doi:10.1016/j.isci.2020.101515 · Cited by 157
Self-citationManaging with Artificial Intelligence: An Integrative Framework
Hillebrand, L., Raisch, S., & Schad, J. (2025) · Academy of Management Annals · doi:10.5465/annals.2022.0072 · Cited by 209
Corporate digital responsibility (CDR) in the age of AI: implications for interactive marketing
Kunz, W. H., & Wirtz, J. (2023) · Journal of Research in Interactive Marketing · doi:10.1108/jrim-06-2023-0176 · Cited by 191
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“” Decades of consumer behavior research sought to shed light on these “ mysterious ways, ” but corresponding research in the fields of machine behavior (Rahwan et al., 2019) and machine psychology (Hagendorff et al., 2023) and fine ‐ tuning impact the silicon sample's quality?”
BackgroundTang, P. M., Koopman, J., Yam, K. C., Cremer, D. D., Zhang, J. H., & Reynders, P. (2022) · Human Resource Management · doi:10.1002/hrm.22154 · Cited by 152
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“the devices have their own behavior [212], which influences the general device behavior.”
BackgroundCulture and low-carbon energy transitions
Sovacool, B. K., & Griffiths, S. (2020) · Nature Sustainability · doi:10.1038/s41893-020-0519-4 · Cited by 144
Information aggregation and collective intelligence beyond the wisdom of crowds
Kameda, T., Toyokawa, W., & Tindale, R. S. (2022) · Nature Reviews Psychology · doi:10.1038/s44159-022-00054-y · Cited by 151
Vulnerable robots positively shape human conversational dynamics in a human–robot team
Traeger, M. L., Strohkorb Sebo, S., Jung, M., Scassellati, B., & Christakis, N. A. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1910402117 · Cited by 140
“Without an understanding of how machines may influence our connections with the people around us, we may encounter negative and unexpected consequences of interacting with these robots (41, 42).”
Self-citationArtificial intelligence, bureaucratic form, and discretion in public service
Bullock, J., Young, M. M., & Wang, Y.-F. (2020) · Information Polity · doi:10.3233/ip-200223 · Cited by 139
Algorithms as co‐workers: Human algorithm role interactions in algorithmic work
Tarafdar, M., Page, X., & Marabelli, M. (2022) · Information Systems Journal · doi:10.1111/isj.12389 · Cited by 145
“By suggesting such new design aspects, our study addresses calls for understanding how algorithms should change in response to ongoing human-algorithm interactions (Rahwan et al., 2019).”
BackgroundArtificial intelligence and corporate ESG performance
Zhang, C., & Yang, J. (2024) · International Review of Economics & Finance · doi:10.1016/j.iref.2024.103713 · Cited by 154
Bayesian modeling of human–AI complementarity
Mark Steyvers, B., Tejeda, H., Kerrigan, G., & Smyth, P. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2111547119 · Cited by 164
Kar, A. K., & Kushwaha, A. K. (2023) · Information Systems Frontiers · doi:10.1007/s10796-021-10219-4 · Cited by 182
“Consequently, AI began attracting more computer science and operation scholars, while management and organization studies focused more on human aspects (Rahwan et al., 2019; Simon, 1987).”
BackgroundBotometer 101: social bot practicum for computational social scientists
Yang, K.-C., Ferrara, E., & Menczer, F. (2022) · Journal of Computational Social Science · doi:10.1007/s42001-022-00177-5 · Cited by 159
“First, characterizing the behavior of and assessing the impact of social bots remains an interesting research topic [35].”
BackgroundMeaningful measures of human society in the twenty-first century
Lazer, D. M. J., Hargittai, E., Freelon, D., González‐Bailón, S., Munger, K., Ognyanova, K., & Radford, J. P. (2021) · Nature · doi:10.1038/s41586-021-03660-7 · Cited by 155
Self-citationFutures of artificial intelligence through technology readiness levels
Martínez-Plumed, F., Gómez, E., & Hernández-Orallo, J. (2021) · Telematics and Informatics · doi:10.1016/j.tele.2020.101525 · Cited by 130
“However, if we take the high edge of this layer, such as performing well in complex machine-human environments, even if only restricted to trading, these are clearly challenging scenarios even for human scientists (Rahwan et al., 2019), so we expect a long time to reach high levels at this layer.”
BackgroundTaylor, J. E. T., & Taylor, G. W. (2020) · Psychonomic Bulletin & Review · doi:10.3758/s13423-020-01825-5 · Cited by 154
“As has been noted, much of the source code for the models and agents we are interested in are proprietary and understandably secret, further complicating the challenge of XAI (Rahwan et al., 2019).”
BackgroundUsers choose to engage with more partisan news than they are exposed to on Google Search
Robertson, R. E., Green, J., Ruck, D. J., Ognyanova, K., Wilson, C., & Lazer, D. (2023) · Nature · doi:10.1038/s41586-023-06078-5 · Cited by 126
Self-citationRecent advances in plant disease detection: challenges and opportunities
Shafay, M., Hassan, T., Owais, M., Hussain, I., Khawaja, S. G., Seneviratne, L. D., & Werghi, N. (2025) · Plant Methods · doi:10.1186/s13007-025-01450-0 · Cited by 129
“Community validation mechanisms, wherein local agricultural experts verify AI-generated diagnoses, facilitate collective trust development and technology adoption [193].”
Meaningful human control: actionable properties for AI system development
Cavalcante Siebert, L., Lupetti, M. L., Aizenberg, E., Beckers, N., Zgonnikov, A., Veluwenkamp, H., Abbink, D., Giaccardi, E., Houben, G.-J., Jonker, C. M., et al. (2023) · AI and Ethics · doi:10.1007/s43681-022-00167-3 · Cited by 138
“Approaches to understand AI ability in context include approaches inspired by human cognitive tests, information theory [85], and ethology (related to animal behavior) [14].”
BackgroundTen recent insights for our understanding of cooperation
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An Integrative Perspective on Algorithm Aversion and Appreciation in Decision-Making
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Developments in X-ray tomography characterization for electrochemical devices
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A comparative study of state-of-the-art driving strategies for autonomous vehicles
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Collective intelligence: a unifying concept for integrating biology across scales and substrates
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“Future work in this area will also continue to be enriched by advances in the collective intelligence of animal behavior 46,213 as well as in the fi eld of swarm robotics 214 – 217 .”
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Explanation as a Social Practice: Toward a Conceptual Framework for the Social Design of AI Systems
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“This, however, is an interdisciplinary challenge [58].”
Towards platform observability
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“If reductionist analysis is impossible for current and future machines, what remains? A consortium of social scientists, computer scientists and ethologists recently called for the creation of a new field, “machine behavior,” in which the best explanations of machines, and predictions of their likely behavior, are a c…”
BackgroundSelf-citationMachine Learning for Human Learners: Opportunities, Issues, Tensions and Threats
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Reinventing Innovation Management: The Impact of Self-Innovating Artificial Intelligence
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“Currently, those scientists who study and implement AI agents into innovation processes “are predominantly the same scientists who have created the agents themselves” [41, p. 477], i.e., computer scientists.”
BackgroundMethod
Metadata
- Authors
- Rahwan, I., Cebrian, M., Obradovich, N., Bongard, J., Bonnefon, J.-F., Breazeal, C., Crandall, J. W., Christakis, N. A., Couzin, I. D., Jackson, M. O., Jennings, N. R., Kamar, E., Kloumann, I. M., Larochelle, H., Lazer, D., McElreath, R., Mislove, A., Parkes, D. C., Pentland, A. ‘., Roberts, M. E., Shariff, A., Tenenbaum, J. B., & Wellman, M.
- Affiliations
- Max Planck Institute for Human Development (DE)
- MIT Media Lab (US)
- University of Vermont (US)
- Centre National de la Recherche Scientifique (FR)
- Brigham Young University (US)
- Yale University (US)
- Max Planck Society (DE)
- Canadian Institute for Advanced Research (CA)
- Imperial College London (GB)
- Microsoft (United States) (US)
- Meta (United States) (US)
- Northeastern University (US)
- Max Planck Institute for Evolutionary Anthropology (DE)
- Harvard University (US)
- University of California San Diego (US)
- University of British Columbia (CA)
- Massachusetts Institute of Technology (US)
- University of Michigan (US)
- Corresponding author
- Rahwan, Iyad
- ORCID
- Rahwan, Iyad
- Obradovich, Nick
- Bongard, Josh
- Bonnefon, Jean-François
- Breazeal, Cynthia
- Crandall, Jacob W.
- Christakis, Nicholas A.
- Couzin, Iain D.
- Jackson, Matthew O.
- Jennings, Nicholas R.
- Lazer, David
- McElreath, Richard
- Mislove, Alan
- Parkes, David C.
- Pentland, Alex ‘Sandy’
- Roberts, Margaret E.
- Shariff, Azim
- Tenenbaum, Joshua B.
- Wellman, Michael
- Venue
- Nature
- Published
- 2019-04
- Volume
- 568
- Issue
- 7753
- Pages
- 477-486
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W4211017739
- Cited by
- 1,364
- References
- 115
- Citations by year
- 2026: 330 · 2025: 258 · 2024: 187 · 2023: 157 · 2022: 142 · 2021: 143 · 2020: 113 · 2019: 31
- FWCI
- 29.97
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- KOPS (University of Konstanz) (submittedVersion)
- HAL (submittedVersion, cc-by)
- Toulouse 1 Capitole Publications (Université Toulouse I Capitole) (submittedVersion)
- Spiral (Imperial College London) (submittedVersion)
- Funders
- National Science Foundation (N00014-14-1-0635, 1355061, 1447634, N00014-09-1-1074, 1231216, 1344227)
- Robert Wood Johnson Foundation
- Office of the Director of National Intelligence (2017-17061500006)
- Future of Life Institute
- Deutsche Forschungsgemeinschaft (422037984)
- Agence Nationale de la Recherche (ANR-17-EURE-0010, 2017-)
- Max-Planck-Gesellschaft (W911NG-11-1-0385, N00014-09-1-1074, IOS-1355061, N00014-14-1-0635)
- Companhia Brasileira de Metalurgia e Mineração (CCF-1231216)
- Defense Advanced Research Projects Agency
- Intelligence Advanced Research Projects Activity (2017-17061500006)
- Office of Naval Research (N00014-09-1-1074, N00014-14-1 0635, IOS-1355061, W911NG-11-1-0385, N00014)
- NIH Office of the Director
- Army Research Office (W911NF-14-1-0431, W911NF-16-1-0304, N00014-14-1-0635, W911NG-11-1- 0385, IOS-1355061, W911NF-16-1-, W911NF, N00014-09-1-1074)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Cybernetics and Technology in Society
- Keywords
- artificial intelligence, artificial intelligence systems, AI regulation, interdisciplinary research agenda, institutional constraints, societal impact of AI
- MeSH terms
- Artificial Intelligence, Humans, Motivation, Robotics
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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