The DOI matches its Crossref record; 115 of the 115 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“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
The cited source says what the claim attributes to it.
In the citing paper
“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
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“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]
Experimental — accuracy not yet measuredIn the citing paper
“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
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Microsoft COCO: Common Objects in Context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_48 · Cited by 45,460
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380
BLEU: a method for automatic evaluation of machine translation
Papineni, K., Roukos, S., Ward, T., & Zhu, W.-J. (2001) · Proceedings of the 40th Annual Meeting on Association for Computational Linguistics - ACL '02 · doi:10.3115/1073083.1073135 · Cited by 21,344
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
Ribeiro, M. T., Singh, S., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939778 · Cited by 17,555
Mastering the game of Go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature · doi:10.1038/nature16961 · Cited by 15,612
Mastering the game of Go without human knowledge
Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., Hubert, T., Baker, L., Lai, M., Bolton, A., et al. (2017) · Nature · doi:10.1038/nature24270 · Cited by 9,084
The spread of true and false news online
Vosoughi, S., Roy, D., & Aral, S. (2018) · Science · doi:10.1126/science.aap9559 · Cited by 8,847
Intriguing properties of neural networks
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., & Fergus, R. (2013) · arXiv · doi:10.48550/arxiv.1312.6199 · Cited by 8,143
The relationship between Precision-Recall and ROC curves
Davis, J., & Goadrich, M. (2006) · Proceedings of the 23rd international conference on Machine learning - ICML '06 · doi:10.1145/1143844.1143874 · Cited by 6,559
Lazer, D. M. J., Baum, M. A., Benkler, Y., Berinsky, A. J., Greenhill, K. M., Menczer, F., Metzger, M. J., Nyhan, B., Pennycook, G., Rothschild, D., et al. (2018) · Science · doi:10.1126/science.aao2998 · Cited by 4,160
Self-citation
Research works printed but not linked: 25
- Simon, H. A. The Sciences of the Artificial (MIT Press, Cambridge, 1969). Simon asks whether there can be a science of the ‘artificial’ that produces knowledge about artificial objects and phenomena.
- Kleinberg, J., Lakkaraju, H., Leskovec, J., Ludwig, J. & Mullainathan, S. Human decisions and machine predictions. Q. J. Econ. 133, 237–293 (2018).
- Amodei, D. et al. Concrete problems in AI safety. Preprint at https://arxiv.org/abs/1606.06565 (2016). · arXiv
- Bessi, A. & Ferrara, E. Social bots distort the 2016 U.S. Presidential election online discussion. First Monday 21, 11 (2016).
- Tufekci, Z. Engineering the public: big data, surveillance and computational politics. First Monday 19, 7 (2014).
- Cartlidge, J., Szostek, C., De Luca, M. & Cliff, D. Too fast too furious—faster financial-market trading agents can give less efficient markets. In Proc. 4th International Conference on Agents and Artificial Intelligence 126–135 (2012).
- Doshi-Velez, F. & Kim, B. Towards a rigorous science of interpretable machine learning. Preprint at https://arxiv.org/abs/1702.08608 (2017). · arXiv
- Kleinberg, J., Mullainathan, S. & Raghavan, M. Inherent trade-offs in the fair determination of risk scores. Preprint at https://arxiv.org/abs/1609.05807 (2016). · arXiv
- Buolamwini, J. & Gebru, T. Gender shades: intersectional accuracy disparities in commercial gender classification. In Proc. 1st Conference on Fairness, Accountability and Transparency (eds Friedler, S. A. & Wilson, C.) 81, 77–91 (PMLR, 2018).
- Bolukbasi, T., Chang, K.-W., Zou, J. Y., Saligrama, V. & Kalai, A. T. Man is to computer programmer as woman is to homemaker? Debiasing word embeddings. In Proc. Advances in Neural Information Processing Systems 4349–4357 (2016).
- Sweeney, L. Discrimination in online ad delivery. Queueing Syst. 11, 10 (2013).
- Ensign, D., Friedler, S. A., Neville, S., Scheidegger, C. & Venkatasubramanian, S. Runaway feedback loops in predictive policing. Preprint at https://arxiv.org/abs/1706.09847 (2017). · arXiv
- Chouldechova, A., Benavides-Prado, D., Fialko, O. & Vaithianathan, R. A case study of algorithm-assisted decision making in child maltreatment hotline screening decisions. In Proc. 1st Conference on Fairness, Accountability and Transparency (eds Friedler, S. A. & Wilson, C.) 81, 134–148 (PMLR, 2018).
- Zhou, Z., Zhang, W. & Wang, J. Inception score, label smoothing, gradient vanishing and -log(D(x)) alternative. Preprint at https://arxiv.org/abs/1708.01729 (2017). · arXiv
- Tinbergen, N. On aims and methods of ethology. Ethology 20, 410–433 (1963).
- Das, R., Hanson, J. E., Kephart, J. O. & Tesauro, G. Agent–human interactions in the continuous double auction. In Proc. 17th International Joint Conference on Artificial Intelligence 1169–1178 (Lawrence Erlbaum, 2001).
- Smilkov, D., Thorat, N., Kim, B., Viégas, F. & Wattenberg, M. SmoothGrad: removing noise by adding noise. Preprint at https://arxiv.org/abs/1706.03825 (2017). · arXiv
- Edwards, H. & Storkey, A. Censoring representations with an adversary. Preprint at https://arxiv.org/abs/1511.05897 (2015). · arXiv
- Zemel, R., Wu, Y., Swersky, K., Pitassi, T. & Dwork, C. learning fair representations. In Proc. International Conference on Machine Learning 325–333 (2013).
- Leibo, J. Z. et al. Psychlab: a psychology laboratory for deep reinforcement learning agents. Preprint at https://arxiv.org/abs/1801.08116 (2018). In this study, the authors use behavioural tools from the life sciences in the study of machine behaviours. · arXiv
- Lazaridou, A., Peysakhovich, A. & Baroni, M. Multi-agent cooperation and the emergence of (natural) language. Preprint at https://arxiv.org/abs/1612.07182 (2016). · arXiv
- Kamar, E., Hacker, S. & Horvitz, E. Combining human and machine intelligence in large-scale crowdsourcing. in Proc. 11th International Conference on Autonomous Agents and Multiagent Systems 467–474 (International Foundation for Autonomous Agents and Multiagent Systems, 2012).
- Wang, D., Khosla, A., Gargeya, R., Irshad, H. & Beck, A. H. Deep learning for identifying metastatic breast cancer. Preprint at https://arxiv.org/abs/1606.05718 (2016). · arXiv
- Pentland, A. Social Physics: How Social Networks Can Make Us Smarter (Penguin, 2015).
- Meyer, M. N. Two cheers for corporate experimentation: The A/B illusion and the virtues of data-driven innovation. Colorado Technol. Law J. 13, 273 (2015).
Cited by
Sorted by citations
Items 601–700 of 1,008
The Importance of Corporate Digital Responsibility in a Digital Service World
Kunz, W. H., Wirtz, J., Hartley, N., & Tarbit, J. (2024) · Emerald Group Publishing Limited eBooks · doi:10.1108/978-1-83753-686-320241011 · Cited by 10
Digital Media and Grassroots Anti-Corruption
Mattoni, A. (2024) · Edward Elgar Publishing eBooks · doi:10.4337/9781802202106 · Cited by 9
GPT-3.5 altruistic advice is sensitive to reciprocal concerns but not to strategic risk
Schmidt, E., Bonati, S., Köbis, N., & Soraperra, I. (2024) · Scientific Reports · doi:10.1038/s41598-024-73306-x · Cited by 9
The Digital Siren’s Call: Accepting Unethical AI Advice
Xu, C., Xu, H., Sun, Y.-Q., & Xiong, W. (2024) · International journal of human computer interactions · doi:10.1080/10447318.2024.2400396 · Cited by 9
“This could lead people to accept their suggestions more readily, even if they lean toward unethical (Rahwan et al., 2019).”
Testing for completions that simulate altruism in early language models
Johnson, T., & Obradovich, N. (2025) · Nature Human Behaviour · doi:10.1038/s41562-025-02258-7 · Cited by 7
“Our investigation used methods from the behavioural sciences 26 because they provided a means of assessing patterns in LLMs’ completions when those language models’ parameters had grown too numerous and their functional forms had grown too intricate for direct inspection 26 .”
Self-citationHuman Factors and Workforce Adaptation in Digital Transition
Subrahmanyam, S. (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-3336-6.ch005 · Cited by 6
THE TRANSFORMATION OF "ARTIFICIAL" SCIENCE INTO ARTIFICIAL INTELLIGENCE: 50 YEARS LATER
Aberšek, B. (2020) · Journal of Baltic Science Education · doi:10.33225/jbse/20.19.340 · Cited by 6
“But machine behaviour (Rahwan et al., 2019) relies more on observations than on engineering knowledge in order to understand the behaviour of AI agents.”
BackgroundPolitical machines: a framework for studying politics in social machines
Papakyriakopoulos, O. (2021) · AI & Society · doi:10.1007/s00146-021-01180-6 · Cited by 8
How AI-Human Symbiotes May Reinvent Innovation and What the New Centaurs Will Mean for Cities
Muller, E. (2022) · Technology and Investment · doi:10.4236/ti.2022.131001 · Cited by 6
Assessing the robustness of critical behavior in stochastic cellular automata
Pontes-Filho, S., Lind, P. G., & Nichele, S. (2022) · Physica D Nonlinear Phenomena · doi:10.1016/j.physd.2022.133507 · Cited by 6
Computational modeling of insight processes and artificial cognitive ontogeny
Red’ko, V. G., Samsonovich, A. V., & Klimov, V. V. (2022) · Cognitive Systems Research · doi:10.1016/j.cogsys.2022.12.004 · Cited by 6
The Paradox of Algorithms and Blame on Public Decision-makers
Ozer, A. L., Waggoner, P., & Kennedy, R. (2024) · Business and Politics · doi:10.1017/bap.2023.35 · Cited by 6
“91 Rahwan et al. (2019).”
Abubshait, A., McDonald, C. G., & Wiese, E. (2021) · Journal of Cognition · doi:10.5334/joc.167 · Cited by 6
Just Don’t Fall: An AI Agent’s Learning Journey Towards Posture Stabilisation
Hossny, M., & Iskander, J. (2020) · AI · doi:10.3390/ai1020019 · Cited by 6
Rich, A. S., Rudin, C. D., Jacoby, D. M. P., Freeman, R., Wearn, O. R., Shevlin, H., Dihal, K., hÉigeartaigh, S. Ó., Butcher, J., Lippi, M., et al. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-019-0141-1 · Cited by 6
Self-citationHuman In Command Machine Learning
Holmberg, L. (2021) · doi:10.24834/isbn.9789178771875 · Cited by 8
Language-based game theory in the age of artificial intelligencePreprint
Capraro, V., Paolo, R. D., Perc, M., & Pizziol, V. (2024) · doi:10.31234/osf.io/cuf8z · Cited by 6
Cugurullo, F. (2024) · Urban Geography · doi:10.1080/02723638.2024.2386179 · Cited by 6
““ Street fi ghts ” are not inevitable, but they are bound to take place unless we carefully examine emerging forms and spatial dynamics of machine behavior (see Rahwan et al., 2019) in the city, and evaluate their compatibility with biological intelligences.”
Xu, H., & Dinev, T. (2022) · MIS Quarterly · doi:10.25300/misq/2022/464e2 · Cited by 6
Humanity and AI: Collaborating for a Flourishing Planet Through Wise Decision-Making
Hallo, L., Hanzis, A., & Rowe, C. (2025) · Challenges · doi:10.3390/challe16010014 · Cited by 6
“Already, financial services are applying autonomous AI decisions in services such as loan approvals, but the incorporation of human judgement in AI-facilitated decision-making is increasingly recognised as a critical domain [47].”
Reputation-based reciprocity in human–bot and human–human networks
Harrell, A., & Traeger, M. L. (2025) · PNAS Nexus · doi:10.1093/pnasnexus/pgaf150 · Cited by 5
“But people increasingly must work with bots and other forms of AI in what have been referred to as “hybrid systems” (19 – 21 ).”
Leib, M., Köbis, N., & Soraperra, I. (2025) · Computers in Human Behavior · doi:10.1016/j.chb.2025.108709 · Cited by 6
Evidence of spillovers from (non)cooperative human-bot to human-human interactions
Harrell, A., & Traeger, M. L. (2025) · iScience · doi:10.1016/j.isci.2025.113006 · Cited by 7
“21 Understanding their direct and indirect impacts on human behavior and connections is imperative to leveraging their utility without sacrificing our humanity.”
Arnold, C., Xu, L. Z., Saffarizadeh, K., & Madiraju, P. (2025) · Behaviour and Information Technology · doi:10.1080/0144929x.2025.2590099 · Cited by 8
“Furthermore, the ethical implications of AI systems simulating human emotions are a subject of ongoing debate (McStay 2018; Rahwan et al. 2019).”
AI and Its Importance in the Development of Luxury Consumption
Pawar, P., Anute, N., Tripathi, S., Ahluwalia, G. K., Kalshetti, P., & Selvakumar, P. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-8422-0.ch001 · Cited by 5
Li, Y., & Ha, H. (2026) · Journal of theoretical and applied electronic commerce research · doi:10.3390/jtaer21010014 · Cited by 6
“This theoretical refinement clarifies not only that hybrid cues work, but why and through which processes they produce greater trust and confidence [27,28].”
AI on the path to good decisions
Kiškis, M. (2026) · Sustainable Futures · doi:10.1016/j.sftr.2026.101644 · Cited by 5
Quan, J., Guo, C., & Wang, X. (2026) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/5.0314278 · Cited by 5
Akiyoshi, M. (2022) · Frontiers in Physics · doi:10.3389/fphy.2022.951296 · Cited by 5
Thinking of learning phenomena as instances of relational behavior
Houwer, J. D., Finn, M., Raemaekers, M., Cummins, J. D., & Boddez, Y. (2023) · Learning & Behavior · doi:10.3758/s13420-022-00567-9 · Cited by 5
MacBehaviour: An R package for behavioural experimentation on large language models
Duan, X., Li, S., & Cai, Z. G. (2024) · Behavior Research Methods · doi:10.3758/s13428-024-02524-y · Cited by 7
“Indeed, the advancement in AI has led to the study of machine behaviour (Binz & Schulz, 2023; Hagendorff, 2023; Huff & Ulakçı, 2024; Rahwan et al., 2019).”
BackgroundMetacognition in the listening brain
Obleser, J. (2025) · Trends in Neurosciences · doi:10.1016/j.tins.2024.12.007 · Cited by 5
“…in line with current Amidst the fast progress in ‘ machine listening ’ and the increasingly ubiquitous role of auditory or language-based human – machine interactions [99], we lack a good model of how forms of ‘ joint ’ metacognition between a listening machine and a human could be achieved.”
Stritzke, W. G. K., & Page, A. C. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-814297-4.00001-7 · Cited by 5
FORKING PATHS OF CULTURAL COMPLEXITY: A CASE STUDY OF RESEARCH PARADIGMS
Borycz, J., Horne, B. D., & Bentley, R. A. (2022) · Advances in Complex Systems · doi:10.1142/s0219525922400045 · Cited by 5
Meyer, S.-C., Hartwig, M., Tisch, A., & Wischniewski, S. (2022) · Nomos Verlagsgesellschaft mbH & Co. KG eBooks · doi:10.5771/9783748927372-313 · Cited by 5
Müller, T. F., Brinkmann, L. U. L., Winters, J., & Pescetelli, N. (2023) · Cognitive Science · doi:10.1111/cogs.13288 · Cited by 5
“The current prevalence and diversity of bots in society are unprecedented (Rahwan et al., 2019) and humans have to increasingly interact with them, especially online.”
BackgroundThe Role of Response Time for Algorithm Aversion in Fast and Slow Thinking Tasks
Lebedeva, A., Kornowicz, J., Lammert, O., & Papenkordt, J. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-35891-3_9 · Cited by 7
Emotion Components and Understanding in Humans and Machines
Bellon, J. (2023) · Technikzukünfte, Wissenschaft und Gesellschaft · doi:10.1007/978-3-658-37641-3_2 · Cited by 6
Research on Artificial Intelligence is Reshaping Our Definition of Morality
Purcell, Z. A., & Bonnefon, J. (2023) · Psychological Inquiry · doi:10.1080/1047840x.2023.2248857 · Cited by 5
Self-citationExploring behavior patterns in human and machine interactions
Zhao, P., Wei, Y., & Wang, S. (2024) · Fundamental Research · doi:10.1016/j.fmre.2023.12.021 · Cited by 5
“The three types of games represent interactions between different agents, constructing a coherent and comparable experiment in deeply understanding HMI [12] .”
Artificial Intelligence and Public Health Context: What We Should Know?
Madli, F., Janin, Y., Gulabdin, S. B., Lada, S., Yun, W. S., Adis, A.-A. A., & Jafar, A. (2024) · Journal of Advanced Research in Applied Sciences and Engineering Technology · doi:10.37934/araset.39.2.96109 · Cited by 7
Lammert, D., Abdullai, L., Betz, S., & Porras, J. (2023) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2023.794 · Cited by 6
Forjan, J., Köbis, N., & Starke, C. (2024) · Edward Elgar Publishing eBooks · doi:10.4337/9781802202106.00020 · Cited by 5
Information Professionals’ Metaphorical Perceptions of Artificial Intelligence Concept
Kavak, A., & Yılmaz, E. (2024) · Journal of Library Administration · doi:10.1080/01930826.2024.2371276 · Cited by 7
“Bongard and Levin (2021) and Rahwan et al. (2019) highlight how the metaphor of “machine behavior” affects the scientific understanding of artificial intelligence by investigating the etiology of artificial intelligence.”
Reynolds rules in swarm fly behavior based on KAN transformer tracking method
Yang, Q., Ji, J., Jing, R., Su, H., Wang, S., & Guo, A. (2025) · Scientific Reports · doi:10.1038/s41598-025-91674-w · Cited by 5
Towards situational awareness from robotic group motion
Levillain, F., St-Onge, D., Beltrame, G., & Zibetti, E. (2019) · IEEE RO-MAN · doi:10.1109/ro-man46459.2019.8956381 · Cited by 5
“This subscribes to the emergent interdisciplinary field of “machine behaviour” [4], which is concerned with the sci-entific study of intelligent machines, not only as engineering artifacts, but as a class of actors with particular behavioural patterns, able to influence human decisions at several…”
BackgroundTransdisciplinary Convergence: Intelligent Infrastructure for Sustainable Development
Shen, Y. (2020) · Data Intelligence · doi:10.1162/dint_a_00063 · Cited by 5
Campagnolo, G. M. (2021) · International Journal of Social Research Methodology · doi:10.1080/13645579.2021.1892379 · Cited by 7
“It is apparent even to its own proponents that data science is at a point where a study of its own ways of working is desperately needed (Rahwan et al., 2019).”
BackgroundETHICAL AND SUSTAINABLE FUTURE OF WORK
Pastor-Escuredo, D., Giacomelli, G., Lumbreras, J., & Garbajosa, J. (2021) · Revista Diecisiete Investigación Interdisciplinar para los Objetivos de Desarrollo Sostenible · doi:10.36852/2695-4427_2021_04.11 · Cited by 5
Algorithmic bias amplification via temporal effects: The case of PageRank in evolving networks
Cui, M., Mariani, M. S., & Medo, M. (2021) · Communications in Nonlinear Science and Numerical Simulation · doi:10.1016/j.cnsns.2021.106029 · Cited by 6
Inductive Biases for Higher-Order Visual Cognition
Shekhar, S. (2022) · The Atrium (University of Guelph) · doi:10.1002/ail2.48 · Cited by 1
Engel, C., Elshan, E., & Ebel, P. (2021) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2021.755 · Cited by 5
“In that, machines face several challenges in conveying social cues in the same manner as humans do [25], which is linked to research that focuses on how anthropomorphic features and behavior of machines affect the relation of a human towards machine agents [29].”
BackgroundBatra, R., Garzorz‐Stark, N., Lauffer, F., Jargosch, M., Pilz, A. C., Roenneberg, S., Schäbitz, A., Böhner, A., Seiringer, P., Thomas, J., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.07.25.221309 · Cited by 5
Exploring the Dynamic Properties of Interaction in Mixed-Initiative Procedural Content Generation
Alvarez, A. (2020) · doi:10.24834/isbn.9789178771400 · Cited by 6
Exploring the Role of Trust during Human-AI Collaboration in Managerial Decision-Making Processes
Tuncer, S. (2022) · doi:10.22215/etd/2022-15336 · Cited by 5
The Soul of the AI: Governance, Ethics, and the Future of Human–AI IntegrationPreprint
Mahajan, P. T. (2025) · doi:10.14293/pr2199.001783.v1 · Cited by 5
Aktürk, E. B. (2025) · OPUS Toplum Araştırmaları Dergisi · doi:10.26466/opusjsr.1666747 · Cited by 4
Park, J.-A., Kim, C. H., & Kim, H.-J. (2025) · Telecommunications Policy · doi:10.1016/j.telpol.2025.103106 · Cited by 5
Ibrahimi, M., Masso, A., & Bellone, M. (2025) · PLoS ONE · doi:10.1371/journal.pone.0335672 · Cited by 4
“Recently, there has been a rising interest in interdisciplinary approaches, like combining anthropological and computational approaches [30], and a need for new methodologies in studying machine behaviour [31], such as the methodological twin-move of making big data thick and thick data big [32].”
Choubey, M., Mishra, S., & Bansal, S. (2026) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-026-05046-x · Cited by 5
When algorithms meet ethics: Mapping consumer moral responses to AI versus human marketing practices
tana, G., Chai, J., Chen, W., & Wauk, G. (2026) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2026.104729 · Cited by 6
Abbas, Z., Zhang, J., & Junaid, M. (2026) · Computers in Human Behavior Reports · doi:10.1016/j.chbr.2026.101057 · Cited by 10
Pimenow, S., Pimenowa, O., Prus, P., & Zieliński, M. (2026) · Sustainability · doi:10.3390/su18104892 · Cited by 5
Elkhatat, A. M., & Al‐Muhtaseb, S. A. (2026) · Applied Intelligence · doi:10.1007/s10489-026-07326-7 · Cited by 5
“…[145 , 146 ] Implementation Challenges Data quality and accessibility [ 31, 109 ] Model interpretability [15 , 136 ] Regulatory compliance [140 , 141] FAIR-aligned practices (findable, accessible, interoper - able, reusable) are now an operational expectation rather than an aspirational…”
InfluentialFlösch, K., Flaisch, T., Imhof, M. A., & Schupp, H. T. (2023) · Psychophysiology · doi:10.1111/psyp.14433 · Cited by 4
Trust in Medical AI: The Case of mHealth Diabetes Apps
Materne, S., Canali, S., & Chiffi, D. (2025) · Journal of Evaluation in Clinical Practice · doi:10.1111/jep.70216 · Cited by 4
“7 Also, AI systems are artefacts that ‘ mediate our social and cultural, but also economic and political interactions ’ [36] and are increasingly and extensively leveraged in a wide range of products and services spanning various domains of daily life, including medicine, justice systems and…”
AI and Workforce Transformation—Opportunities and Challenges
Mohieldin, M., González-Pérez, M. A., & Zahran, M. (2025) · Springer eBooks · doi:10.1007/978-3-031-93357-8_6 · Cited by 4
Artificial intelligence and value-based strategy: a literature review and future research directions
Islami, X., Mulolli, E., & Mustafa, N. (2025) · Management Review Quarterly · doi:10.1007/s11301-025-00549-x · Cited by 6
Huang, Y.-K., Liang, J., & Lu, C. (2025) · Travel Behaviour and Society · doi:10.1016/j.tbs.2025.101143 · Cited by 4
Identifying and addressing unintended values when designing (with) Artificial Intelligence
Stoimenova, N., & Kleinsmann, M. S. (2020) · Proceedings of DRS · doi:10.21606/drs.2020.222 · Cited by 6
Im, H., Jeon, S., Cho, H., Shin, S.-Y., Choi, D., & Hong, H. (2022) · International ACM SIGGROUP Conference on Supporting Group Work (GROUP) · doi:10.1145/3565967.3570973 · Cited by 4
“To anticipate and prevent these unexpected consequences caused by user interaction with AI models, developers desire to understand the ways potential users and contexts shape AI’s behavior [4].”
BackgroundSelf-Governing Hybrid Societies and Deception
Sarkadi, Ş. (2024) · ACM Transactions on Autonomous and Adaptive Systems · doi:10.1145/3638549 · Cited by 5
“In this article, we answer these questions using large-scale evolutionary agent-based simulations, similarly used to analyse agent societies in References [1, 40, 43, 46], and interpret the results from the perspective of Machine Behaviour ( MB ) [37].”
BackgroundMethodAlgorithmic trading, what if it is just an illusion? Evidence from experimental asset markets
Leal, S. J., & Hanaki, N. (2024) · Journal of Behavioral and Experimental Economics · doi:10.1016/j.socec.2024.102240 · Cited by 4
AI agents can coordinate beyond human scale
De Marzo, G., Castellano, C., & Garcia, D. (2024) · arXiv · doi:10.48550/arxiv.2409.02822 · Cited by 4
Billiris, G. V., Gill, A. Q., Oppermann, I., & Niazi, M. (2024) · Digital Government Research and Practice · doi:10.1145/3703459 · Cited by 4
“The UK’s adaptive, sector-specific strategies, though flexible, risk creating disparities, necessitating stringent oversight for consistency [ 26 , 32 ].”
How Might Artificial Intelligence Influence Human Evolution?
Brooks, R. C. (2024) · The Quarterly Review of Biology · doi:10.1086/733290 · Cited by 6
Building an Ethical AI Culture Within Organizations
Yadav, R. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-9894-4.ch001 · Cited by 4
Climate Change, Intergenerational Fairness, and the Promises and Pitfalls of Artificial Intelligence
Hauser, O. (2025) · Environmental and Resource Economics · doi:10.1007/s10640-025-00986-y · Cited by 4
Nugent, S. E. (2025) · AI & Society · doi:10.1007/s00146-025-02310-0 · Cited by 4
Rizzo, M. (2025) · Annals of Neurology · doi:10.1002/ana.27229 · Cited by 6
Human and artificial intelligence to illuminate MDS
Haferlach, T. (2020) · Blood · doi:10.1182/blood.2020008742 · Cited by 4
Random choices facilitate solutions to collective network coloring problems by artificial agents
Jones, M. I., Pauls, S. D., & Fu, F. (2021) · iScience · doi:10.1016/j.isci.2021.102340 · Cited by 5
“Thus, the system will be allowed to settle into the global solution, even when individuals playing random update rules are likely to choose a random color.”
BackgroundRisk sensitivity and theory of mind in human coordination
Ferreira, P. L., Santos, F. C., & Pequito, S. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009167 · Cited by 4
Attainment Regions in Feature-Parameter Space for High-Level Debugging in Autonomous Robots
Smith, S. C., & Ramamoorthy, S. (2021) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros51168.2021.9636336 · Cited by 4
“Another objective of the region analysis is to explain the decision process of the system [13].”
BackgroundSai, B. K., Mayer, Y. T., & Bauernhansl, T. (2021) · Procedia CIRP · doi:10.1016/j.procir.2021.11.061 · Cited by 4
Innes, C. J., Innes, J. M., & Morrison, B. W. (2022) · Australian and New Zealand Journal of European Studies · doi:10.30722/anzjes.vol14.iss1.15852 · Cited by 4
Research on the Trend of Automotive User Experience
Liu, A., & Tan, H. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-06053-3_13 · Cited by 4
IV. Informationsbezogene Tätigkeiten
Tegtmeier, P., Certa, M., & Wischniewski, S. (2022) · Nomos Verlagsgesellschaft mbH & Co. KG eBooks · doi:10.5771/9783748927372-133 · Cited by 4
Stingy bots can improve human welfare in experimental sharing networks
Shirado, H., Hou, Y. T.-Y., & Jung, M. F. (2023) · Scientific Reports · doi:10.1038/s41598-023-44883-0 · Cited by 5
The Effect of Artificial Intelligence on the Accounting Profession
Abdulhay, D. S. (2024) · Advances in finance, accounting, and economics book series · doi:10.4018/979-8-3693-0847-9.ch010 · Cited by 4
Response Time Analysis for Explainability of Visual Processing in CNNs
Taylor, E., Shekhar, S., & Taylor, G. W. (2020) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00199 · Cited by 4
“The majority of techniques developed for XAI depend on privileged access to the architecture and parameters of the model in question [1].”
BackgroundRole of Resource Production in Community of People and Robots
Kotov, V. B., & Сохова, З. Б. (2020) · Studies in computational intelligence · doi:10.1007/978-3-030-60577-3_11 · Cited by 4
Explorative Experiments: A Paradigm Shift to Deal with Severe Uncertainty in Autonomous Robotics
Schiaffonati, V. (2022) · Perspectives on Science · doi:10.1162/posc_a_00415 · Cited by 4
“To overcome this opacity a novel approach labelled as “ Machine Behavior ” has been recently proposed (Rahwan et al. 2019).”
MethodCabour, G., Ledoux, É., & Bassetto, S. (2022) · Applied Ergonomics · doi:10.1016/j.apergo.2022.103703 · Cited by 4
Duarte, F., & Álvarez, R. (2021) · The MIT Press eBooks · doi:10.7551/mitpress/12418.001.0001 · Cited by 4
Psychological targeting in the age of Big Data
Appel, R. E., & Matz, S. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-819200-9.00015-6 · Cited by 4
Ong, K. S. H., Wang, W., Friedrichs, T., & Niyato, D. T. (2021) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc52423.2021.9658936 · Cited by 4
“Learning from how psychologists investigate human behavior within the operating environment [3], both the RL agent and human behavior cannot be fully understood without observing the circumstances in which the observed behavior occurs [18].”
BackgroundHigh Frequency Trading and LearningPreprint
Arifovic, J., Chiarella, C., He, X.-Z. '., & Wei, L. (2016) · SSRN Electronic Journal · doi:10.2139/ssrn.2771153 · Cited by 4
Magenheim, J., & Schulte, C. (2020) · Springer eBooks · doi:10.1007/978-3-030-10576-1_253 · Cited by 5
Comprehensive Literature Study On Industry 4.0Preprint
Sakib, S. M. N. (2022) · doi:10.33774/coe-2022-805gz · Cited by 4
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
