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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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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).
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Wagner, C., Strohmaier, M., Olteanu, A., Kıcıman, E., Contractor, N., & Eliassi-Rad, T. (2021) · Nature · doi:10.1038/s41586-021-03666-1 · Cited by 144
Tassiello, V., Tillotson, J. S., & Rome, A. (2021) · Psychology and Marketing · doi:10.1002/mar.21488 · Cited by 107
“…to improve and create seamless and frictionless customer experiences with VAs be that through improving emotional connections (Chung et al., 2018), sensorial experiences (Mishra et al., 2020), engagement (Moriuchi, 2019), trust (Pitardi & Marriott, 2021), or companionship (Ramadan et al., 2021).”
BackgroundAdolescent risk-taking in the context of exploration and social influence
Ciranka, S., & van den Bos, W. (2021) · Developmental Review · doi:10.1016/j.dr.2021.100979 · Cited by 127
Research integrity in the era of artificial intelligence: Challenges and responses
Chen, Z., Chen, C., Yang, G., He, X., Chi, X., Zeng, Z., & Chen, X. (2024) · Medicine · doi:10.1097/md.0000000000038811 · Cited by 109
“24] Applications in social sciences, AI application extends to the social sciences, analyzing large-scale data to decipher patterns in human behavior.”
BackgroundSchlicker, N., Langer, M., Ötting, S. K., Baum, K., König, C. J., & Wallach, D. (2021) · Computers in Human Behavior · doi:10.1016/j.chb.2021.106837 · Cited by 106
Pedreschi, D., Pappalardo, L., Ferragina, E., Baeza-Yates, R., Barabási, A.-L., Dignum, F., Dignum, V., Eliassi-Rad, T., Giannotti, F., Kertész, J., et al. (2025) · Artificial Intelligence · doi:10.1016/j.artint.2024.104244 · Cited by 102
“First, it expands the debate on “machine behaviour”, i.e., the study of behaviour exhibited by intelligent machines [75], illustrating that the feedback loop is the key process shaping human-AI coevolution.”
Self-citationWang, J., Cui, P., Zhang, J., Ge, Y., Liu, X., Xuan, N., Gu, G., Cheng, G., & Du, Z. (2021) · Nano Energy · doi:10.1016/j.nanoen.2021.106320 · Cited by 102
Deception abilities emerged in large language models
Hagendorff, T. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2317967121 · Cited by 100
“Hence, one can purely rely on behavioral patterns (43) or “functional deception” (44), meaning that LLMs output signals as if they had intentions that lead to deceptive behavior (45).”
BackgroundHuman social sensing is an untapped resource for computational social science
Galešić, M., de Bruin, W. B., Dalege, J., Feld, S. L., Kreuter, F., Olsson, H., Prelec, D., Stein, D. L., & van der Does, T. (2021) · Nature · doi:10.1038/s41586-021-03649-2 · Cited by 106
Colombo, M., & Piccinini, G. (2023) · doi:10.1017/9781009183734 · Cited by 106
Levin, M. (2021) · Biochemical and Biophysical Research Communications · doi:10.1016/j.bbrc.2020.10.077 · Cited by 92
Izu, C., Schulte, C., Aggarwal, A., Cutts, Q. I., Duran, R., Gutica, M., Heinemann, B., Kraemer, E. T., Lonati, V., Mirolo, C., et al. (2019) · ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE) · doi:10.1145/3344429.3372501 · Cited by 105
“…to in a variety of formulations throughout different fields, as shown in Table 1. a Originally used in Biology, now adapted for machine behavior [88] From an educational viewpoint, it is interesting to observe that the three dimensions of the Block Model also correlate with categories…”
BackgroundPassive microfluidic devices for cell separation
Zhang, T., Carlo, D. D., Lim, C. T., Zhou, T., Tian, G., Tang, T., Shen, A. Q., Li, W., Li, M., Yang, Y., et al. (2024) · Biotechnology Advances · doi:10.1016/j.biotechadv.2024.108317 · Cited by 97
Artificial intelligence in government: Concepts, standards, and a unified framework
Straub, V. J., Morgan, D., Bright, J., & Margetts, H. Z. (2023) · Government Information Quarterly · doi:10.1016/j.giq.2023.101881 · Cited by 154
Pellert, M., Lechner, C. M., Wagner, C., Rammstedt, B., & Strohmaier, M. (2024) · Perspectives on Psychological Science · doi:10.1177/17456916231214460 · Cited by 97
“…studies of not only the cognitive (Binz & Schulz, 2022) but also these noncognitive characteristics of LLMs and to establish how they relate to behavior and decisions of LLMs in downstream tasks, a goal that is echoed in the related research program of “machine behavior” (Rahwan et al., 2019).”
Levin, M. G. (2023) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-023-04790-z · Cited by 118
Zhan, E. S., Molina, M. D., Rheu, M., & Peng, W. (2023) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2023.2261731 · Cited by 112
“For example, people who fear that AI has negative effects on policing, surveillance, and financial systems may vote or protest the government adopting AI-assisted systems (Rahwan et al., 2019).”
Language-based game theory in the age of artificial intelligence
Capraro, V., Paolo, R. D., Perc, M., & Pizziol, V. (2024) · Journal of The Royal Society Interface · doi:10.1098/rsif.2023.0720 · Cited by 97
Towards Risk‐Free Trustworthy Artificial Intelligence: Significance and Requirements
Alzubaidi, L., Al-Sabaawi, A., Bai, J., Dukhan, A., Alkenani, A. H., Al‐Asadi, A., Alwzwazy, H. A., Manoufali, M., Fadhel, M. A., Albahri, A. S., et al. (2023) · International Journal of Intelligent Systems · doi:10.1155/2023/4459198 · Cited by 116
Cognitive imperialism in artificial intelligence: counteracting bias with indigenous epistemologies
Ofosu-Asare, Y. (2025) · AI & SOCIETY · doi:10.1007/s00146-024-02065-0 · Cited by 97
Challenges of human—machine collaboration in risky decision-making
Xiong, W., Fan, H., Ma, L., & Wang, C. (2022) · Frontiers of Engineering Management · doi:10.1007/s42524-021-0182-0 · Cited by 95
“In a hybrid system, machine behaviors in fl uence and shape human behaviors and vice versa (Rahwan et al., 2019).”
BackgroundInfluentialSerna, I., Morales, A., Fiérrez, J., & Obradovich, N. (2022) · Artificial Intelligence · doi:10.1016/j.artint.2022.103682 · Cited by 88
Self-citationReward and punishment in climate change dilemmas
Góis, A. R., Santos, F. P., Pacheco, J. M., & Santos, F. C. (2019) · Scientific Reports · doi:10.1038/s41598-019-52524-8 · Cited by 89
Hagendorff, T., Bossert, L. N., Tse, Y. F., & Singer, P. (2022) · AI and Ethics · doi:10.1007/s43681-022-00199-9 · Cited by 102
Improving Human-AI Collaboration With Descriptions of AI Behavior
Cabrera, Á. A., Perer, A., & Hong, J. I. (2023) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3579612 · Cited by 87
“Behavioral analysis enables developers to go beyond aggregate metrics, such as accuracy, to discover specific and important behaviors [11, 49].”
BackgroundA new sociology of humans and machines
Tsvetkova, M., Yasseri, T., Pescetelli, N., & Werner, T. (2024) · Nature Human Behaviour · doi:10.1038/s41562-024-02001-8 · Cited by 122
“M the ecological approach to studying machine behaviour 16 , the hybrid collective intelligence perspective 17 and the budding field of social AI 18 .”
Opportunities and risks of large language models in psychiatry
Obradovich, N., Khalsa, S. S., Khan, W. U., Suh, J., Perlis, R. H., Ajilore, O., & Paulus, M. P. (2024) · NPP—Digital Psychiatry and Neuroscience · doi:10.1038/s44277-024-00010-z · Cited by 103
“Given the complex determinants of population mental health, such broad-scale social implications [17] require holistic evaluation.”
Self-citationRiveiro, M., & Thill, S. (2021) · Artificial Intelligence · doi:10.1016/j.artint.2021.103507 · Cited by 93
Quantifying the use and potential benefits of artificial intelligence in scientific research
Gao, J., & Wang, D. (2024) · Nature Human Behaviour · doi:10.1038/s41562-024-02020-5 · Cited by 81
Artificial Intelligence, Governance and Ethics: Global PerspectivesPreprint
Daly, A., Hagendorff, T., Li, H., Mann, M., Marda, V., Wagner, B., Wang, W., & Witteborn, S. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3414805 · Cited by 91
Guliyev, H. (2023) · Research in Globalization · doi:10.1016/j.resglo.2023.100140 · Cited by 99
Moral Uncanny Valley: A Robot’s Appearance Moderates How its Decisions are Judged
Laakasuo, M., Palomäki, J., & Köbis, N. (2021) · International Journal of Social Robotics · doi:10.1007/s12369-020-00738-6 · Cited by 69
Promises and Perils of Experimentation: The Mutual-Internal-Validity Problem
Lin, H., Werner, K. M., & Inzlicht, M. (2021) · Perspectives on Psychological Science · doi:10.1177/1745691620974773 · Cited by 82
Algorithm exploitation: Humans are keen to exploit benevolent AI
Karpus, J., Krüger, A., Verba, J. T., Bahrami, B., & Deroy, O. (2021) · iScience · doi:10.1016/j.isci.2021.102679 · Cited by 76
The promise and perils of using artificial intelligence to fight corruption
Köbis, N., Starke, C., & Rahwan, I. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-022-00489-1 · Cited by 127
Self-citationMarch, C. (2021) · Journal of Economic Psychology · doi:10.1016/j.joep.2021.102426 · Cited by 72
Chakraborty, D., Polisetty, A., Sowmya, G., Rana, N. P., & Khorana, S. (2024) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2024.103842 · Cited by 99
Human-machine symbiosis: A multivariate perspective for physically coupled human-machine systems
Inga, J., Rueß, M., Robens, J. H., Nelius, T., Rothfuß, S., Kille, S., Dahlinger, P., Lindenmann, A., Thomaschke, R., Neumann, G. M., et al. (2022) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2022.102926 · Cited by 85
The Notorious GPT: science communication in the age of artificial intelligence
Schäfer, M. S. (2023) · Journal of Science Communication · doi:10.22323/2.22020402 · Cited by 67
“A general point to be learned from the meteoric rise of generative AI, is that we should all be continuously on the lookout for emerging socio-technical innovations that may impact (science) communication [Schäfer & Wessler, 2020; cf. Rahwan et al., 2019].”
Exploring the psychology of LLMs’ moral and legal reasoning
Almeida, G. F. C. F., Nunes, J. L., Engelmann, N., Wiegmann, A., & Araújo, M. D. (2024) · Artificial Intelligence · doi:10.1016/j.artint.2024.104145 · Cited by 70
Reasoning about responsibility in autonomous systems: challenges and opportunities
Yazdanpanah, V., Gerding, E., Stein, S., Dastani, M. M., Jonker, C. M., Norman, T. J., & Ramchurn, S. D. (2022) · AI & Society · doi:10.1007/s00146-022-01607-8 · Cited by 71
“…artificial intelligence (AI), the academic community argues that it is crucial to coordinate the behaviour of AI systems (Bryson and Winfield 2017; Rahwan et al. 2019), to ensure their compatibility with our social values (Russell 2019; Ramchurn et al. 2021), and to design verifiably safe and…”
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Leib, M., Köbis, N., Rilke, R. M., Hagens, M., & Irlenbusch, B. (2023) · The Economic Journal · doi:10.1093/ej/uead056 · Cited by 80
Understanding Human-AI Augmentation in the Workplace: A Review and a Future Research Agenda
Trinh, N. T. T., & Elbanna, A. R. (2025) · Information Systems Frontiers · doi:10.1007/s10796-025-10591-5 · Cited by 109
Learning to run a power network challenge for training topology controllers
Marot, A., Donnot, B., Romero, C., Donon, B., Lerousseau, M., Veyrin-Forrer, L., & Guyon, I. (2020) · Electric Power Systems Research · doi:10.1016/j.epsr.2020.106635 · Cited by 70
Predictive coding and stochastic resonance as fundamental principles of auditory phantom perception.
Schilling, A., Sedley, W., Gerum, R., Metzner, C., Tziridis, K., Maier, A., Schulze, H., Zeng, F.-G., Friston, K. J., & Krauss, P. (2023) · Brain · doi:10.1093/brain/awad255 · Cited by 70
“114 Finally, neuroscience may even serve to investigate machine behaviour, 115 i.e. illuminate the black box of deep learning.”
BackgroundValues and Ethics in Information Systems
Spiekermann, S., Krasnova, H., Hinz, O. F., Baumann, A., Benlian, A., Gimpel, H., Heimbach, I., Köster, A., Maedche, A., Niehaves, B., et al. (2022) · Business & Information Systems Engineering · doi:10.1007/s12599-021-00734-8 · Cited by 82
“However, there is also the risk that decision-making supported or automated by algorithms may unintentionally and unexpectedly shape societal outcomes for the worse (see Rahwan et al. (2019) for a discussion).”
BackgroundAI ethics and systemic risks in finance
Svetlova, E. (2022) · AI and Ethics · doi:10.1007/s43681-021-00129-1 · Cited by 65
“Indeed, collective machine behaviour as well as hybrid human–machine behaviour might initiate negative emergent effects at the level of financial system as a whole [3].”
BackgroundSpontaneous Perspective Taking Toward Robots: The Unique Impact of Humanlike Appearance
Zhao, X., & Malle, B. F. (2019) · Cognition · doi:10.1016/j.cognition.2022.105076 · Cited by 75
Hagendorff, T., Dasgupta, I., Binz, M., Chan, S. C. Y., Lampinen, A., Wang, J. X., Akata, Z., & Schulz, E. (2023) · arXiv · doi:10.48550/arxiv.2303.13988 · Cited by 68
From automation to autonomy and autonomous vehicles
Xu, W. M. (2020) · interactions · doi:10.1145/3434580 · Cited by 72
“They can exhibit behaviors and evolve to gain certain levels of human-like cognitive, self-executing, and adaptive abilities [3].”
BackgroundSynthetic living machines: A new window on life
Ebrahimkhani, M. R., & Levin, M. G. (2021) · iScience · doi:10.1016/j.isci.2021.102505 · Cited by 75
Analysis of Q-Learning Like Algorithms Through Evolutionary Game Dynamics
Shi, Y., & Rong, Z. (2022) · IEEE Transactions on Circuits & Systems II Express Briefs · doi:10.1109/tcsii.2022.3161655 · Cited by 72
Yue, B., & Li, H. (2023) · Frontiers in Psychology · doi:10.3389/fpsyg.2023.1277861 · Cited by 72
“Scholars in the field of computer science focus on maximizing AI autonomy in Human-AI Collaboration, striving for AI’s independent decision-making capability (Rahwan et al., 2019, 477–486).”
BackgroundPereira, F. D., Fonseca, S. C., Oliveira, E. H. T., Cristea, A. I., Bellhauser, H., Rodrigues, L., Oliveira, D. B. F., Isotani, S., & Carvalho, L. S. G. (2021) · IEEE Access · doi:10.1109/access.2021.3105956 · Cited by 63
“…not only on prediction performance but also on the interpretation of the model’s decision, towards Explainable AI [39], [41] and Machine Behaviour [54] study; Detecting, for the first time, early effective and ineffective programming behaviours at single-student granularity level; A new clustering…”
BackgroundDubé, S., & Anctil, D. (2020) · International Journal of Social Robotics · doi:10.1007/s12369-020-00706-0 · Cited by 68
Artificial Intelligence in Education
Flogie, A., & Aberšek, B. (2021) · IntechOpen eBooks · doi:10.5772/intechopen.96498 · Cited by 66
Blame Attribution Asymmetry in Human–Automation Cooperation
Liu, P., & Du, Y. L. (2021) · Risk Analysis · doi:10.1111/risa.13674 · Cited by 65
Psychoanalyzing artificial intelligence: the case of Replika
Possati, L. M. (2023) · AI & SOCIETY · doi:10.1007/s00146-021-01379-7 · Cited by 65
“There are “fundamental theoretical limits to our ability to verify that a particular piece of code will always satisfy desirable properties, unless we execute the code, and observe its behavior” (Rahwan et al. 2019, 3).”
BackgroundTrustworthy human-AI partnerships
Ramchurn, S. D., Stein, S., & Jennings, N. R. (2021) · iScience · doi:10.1016/j.isci.2021.102891 · Cited by 73
“, 2021; Shneiderman, 2020; Wilson and Daugherty, 2018) and machine-centred approaches (Rahwan et al., 2019; Awad et al., 2018; Kraus et al., 2020) to the development of artificial intelligence have emerged.”
BackgroundSelf-citationWang, G., Guo, Y., Zhang, W., Xie, S., & Chen, Q. (2023) · Government Information Quarterly · doi:10.1016/j.giq.2023.101803 · Cited by 75
Tracing the legitimacy of Artificial Intelligence: A longitudinal analysis of media discourse
Korneeva, E., Salge, T. O., Teubner, T., & Antons, D. (2023) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2023.122467 · Cited by 76
Superintelligence Cannot be Contained: Lessons from Computability Theory
Alfonseca, M., Cebrián, M., Anta, A. F., Coviello, L., Abeliuk, A., & Rahwan, I. (2021) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.12202 · Cited by 64
Self-citationMore-Than-Human Design and AI: In Conversation with Agents
Nicenboim, I., Giaccardi, E., Søndergaard, M. L. J., Reddy, A. V., Strengers, Y., Pierce, J., & Redström, J. (2020) · Companion Publication of the 2020 ACM Designing Interactive Systems Conference · doi:10.1145/3393914.3395912 · Cited by 63
“In parallel, novel ethnographic approaches have been proposed for studying algorithms as culture [17] or observing machine behavior in the same way we study human or animal behavior [15].”
BackgroundGacu, J. G., Monjardin, C. E. F., Mangulabnan, R. G. T., Pugat, G. C. E., & Solmerin, J. G. (2025) · Water · doi:10.3390/w17111707 · Cited by 83
A cognitive approach to human–AI complementarity in dynamic decision-making
González, C., & Heidari, H. (2025) · Nature Reviews Psychology · doi:10.1038/s44159-025-00499-x · Cited by 73
Fintz, M., Osadchy, M., & Hertz, U. (2022) · Scientific Reports · doi:10.1038/s41598-022-08863-0 · Cited by 68
Puranik, A., Dandekar, P. P., & Jain, R. D. (2022) · Biotechnology Progress · doi:10.1002/btpr.3291 · Cited by 63
Verma, H., Mlynář, J., Schaer, R., Reichenbach, J., Jreige, M., Prior, J. O., Évéquoz, F., & Depeursinge, A. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581506 · Cited by 70
“The forces driving rapid advancements in AI are inherently disconnected from the study of their impact on individuals, societies, and organizations [76].”
BackgroundDiscovering and Validating AI Errors With Crowdsourced Failure Reports
Cabrera, Á. A., Druck, A. J., Hong, J., & Perer, A. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3479569 · Cited by 68
There’s Plenty of Room Right Here: Biological Systems as Evolved, Overloaded, Multi-Scale Machines
Bongard, J., & Levin, M. G. (2023) · Biomimetics · doi:10.3390/biomimetics8010110 · Cited by 75
Self-citationBaker, B., Mills, K. A., McDonald, P., & Wang, L. (2023) · Teachers College Record The Voice of Scholarship in Education · doi:10.1177/01614681231191291 · Cited by 71
“Indeed, a wide range of machine learning scholars have recently called for a new paradigm of research into “machine behavior” modeled on animal ethology that studies the environmental and social contexts with which a system interacts (Rahwan et al., 2019).”
BackgroundLi, Y., Song, Y., Sun, Y., & Zeng, M. (2024) · Technology in Society · doi:10.1016/j.techsoc.2024.102518 · Cited by 67
Predicting the moral consideration of artificial intelligences
Pauketat, J. V. T., & Anthis, J. R. (2022) · Computers in Human Behavior · doi:10.1016/j.chb.2022.107372 · Cited by 60
Visual cognition in multimodal large language models
Schulze Buschoff, L. M., Akata, E., Bethge, M., & Schulz, E. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-024-00963-y · Cited by 83
“C: Used multi-modal large language models and their size. learning models 85–87 .”
Recent Progress on the Convergence of the Internet of Things and Artificial Intelligence
Shi, F., Ning, H., Huangfu, W., Zhang, F., Wei, D., Hong, T., & Daneshmand, M. (2020) · IEEE Network · doi:10.1109/mnet.011.2000009 · Cited by 60
“Artificial intelligence (AI) serves as a technical earthquake at the right time, providing humans with augmented welfare and well being, and it has been demonstrated that AI does have strong capability in different aspects like face recognition, credit scoring, decision making, and autonomous driving [1].”
BackgroundSpatiotemporal Learning of Multivehicle Interaction Patterns in Lane-Change Scenarios
Zhang, C., Zhu, J.-C., Wang, W., & Xi, J. (2020) · IEEE transactions on intelligent transportation systems (Print) · doi:10.1109/tits.2021.3057645 · Cited by 59
The Data-Driven Workplace and the Case for Worker Technology Rights
Bernhardt, A. D., Kresge, L., & Suleiman, R. (2022) · Industrial and Labor Relations Review · doi:10.1177/00197939221131558 · Cited by 59
“But recent advancements in artificial intelligence research have resulted in much more complex algorithms that enable computer systems to learn, reason, and interact with humans in their environment (Rahwan et al. 2019).”
BackgroundChoung, H., Seberger, J. S., & David, P. (2023) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2023.2266244 · Cited by 73
“1 For example, ADM is routinely used in college admissions (Marcinkowski et al., 2020), news recommendations (Thurman et al., 2019), and a vast array of contexts including policy, health, finance, and employment (Araujo et al., 2020; Rahwan et al., 2019).”
AI Trustworthiness in Manufacturing: Challenges, Toolkits, and the Path to Industry 5.0
Ahangar, M. N., Farhat, Z. A., & Sivanathan, A. (2025) · Sensors · doi:10.3390/s25144357 · Cited by 73
“Since accountability is a dynamic process [117,118], establishing it requires comprehensive governance throughout the AI lifecycle and active collaboration among all stakeholders [119].”
Song, X., Gu, H., Li, Y., Leung, X. Y., & Ling, X. (2023) · Information Technology & Tourism · doi:10.1007/s40558-023-00275-8 · Cited by 59
“As new technologies, service robots are increasingly integrating into our society, and people have more opportunities to interact with them (Luo et al. 2019; Rahwan et al. 2019 ).”
Zombies in the Loop? Humans Trust Untrustworthy AI-Advisors for Ethical Decisions
Krügel, S., Ostermaier, A., & Uhl, M. (2022) · Philosophy & Technology · doi:10.1007/s13347-022-00511-9 · Cited by 76
“AI-powered algorithms have conquered areas such as personnel recruitment, the allocation of loans, penal sentencing, or autonomous driving (Rahwan et al., 2019).”
BackgroundAwad, E., Levine, S., Anderson, M., Anderson, S., Conitzer, V., Crockett, M. J., Everett, J. A. C., Evgeniou, T., Alison Gopnik, C., Jamison, J., et al. (2022) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2022.02.009 · Cited by 70
Self-citationFleming, R. W., & Storrs, K. R. (2019) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2019.07.004 · Cited by 54
Virvou, M., Tsihrintzis, G. A., & Tsichrintzi, E.-A. (2024) · Information Sciences · doi:10.1016/j.ins.2024.120759 · Cited by 55
The signature-testing approach to mapping biological and artificial intelligences
Taylor, A. H., Bastos, A. P. M., Brown, R. L., & Allen, C. (2022) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2022.06.002 · Cited by 64
Intrinsic Noise Improves Speech Recognition in a Computational Model of the Auditory Pathway
Schilling, A., Gerum, R. C., Zankl, A., Metzner, C., Maier, A., & Krauß, P. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.908330 · Cited by 52
Attention: How high-frequency trading improves price efficiency following earnings announcements
Chakrabarty, B., Moulton, P. C., & Wang, X. A. (2021) · Journal of Financial Markets · doi:10.1016/j.finmar.2021.100690 · Cited by 54
Social Network Interventions for HIV Transmission Elimination
Pagkas-Bather, J., Young, L. E., Chen, Y., & Schneider, J. A. (2020) · Current HIV/AIDS Reports · doi:10.1007/s11904-020-00524-z · Cited by 53
AI for Everyone? Critical Perspectives
Verdegem, P., Kaplan, A., Hofkirchner, W., Ng, J., McQuillan, D., Rehak, R., Daly, A., Devit, S. K., Mann, M., Steinhoff, J., et al. (2021) · University of Westminster Press eBooks · doi:10.16997/book55 · Cited by 68
To Trust or Not To Trust: How a Conversational Interface Affects Trust in a Decision Support System
Gupta, A., Basu, D., Ghantasala, R., Qiu, S., & Gadiraju, U. (2022) · International World Wide Web Conference (WWW) · doi:10.1145/3485447.3512248 · Cited by 61
“Assistive technologies like decision support systems help humans in making decisions and provide the best course of action, particularly when dealing with large amounts of data and complex variables [13, 38, 43].”
BackgroundZeno: An Interactive Framework for Behavioral Evaluation of Machine Learning
Cabrera, Á. A., Fu, E., Bertucci, D., Holstein, K., Talwalkar, A., Hong, J. I., & Perer, A. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581268 · Cited by 51
Black Boxes or Unflattering Mirrors? Comparative Bias in the Science of Machine Behaviour
Buckner, C. J. (2021) · The British Journal for the Philosophy of Science · doi:10.1086/714960 · Cited by 59
Delegation to artificial intelligence can increase dishonest behaviour
Köbis, N., Rahwan, Z., Rilla, R., Supriyatno, B. I., Bersch, C., Ajaj, T., Bonnefon, J., & Rahwan, I. (2025) · Nature · doi:10.1038/s41586-025-09505-x · Cited by 80
Self-citationSocio-Cognitive Engineering of a Robotic Partner for Child's Diabetes Self-Management
Neerincx, M. A., van Vught, W., Henkemans, O. B., Oleari, E., Broekens, J., Peters, R., Kaptein, F., Demiris, Y., Kiefer, B., Fumagalli, D., et al. (2019) · Frontiers in Robotics and AI · doi:10.3389/frobt.2019.00118 · Cited by 58
Pedagogical framework for cultivating children's data agency and creative abilities in the age of AI
Kahila, J., Vartiainen, H., Tedre, M., Arkko, E., Lin, A., Pope, N., Jormanainen, I., & Valtonen, T. (2024) · Informatics in Education · doi:10.15388/infedu.2024.15 · Cited by 54
“Traditional rule-based automation has been accompanied by artificial intelligence (AI), particularly machine learning (ML), which has been accelerating automation in society, the workplace, and people's daily lives (Denning and Tedre, 2019; Rahwan et al., 2019; Tedre et al., 2020).”
BackgroundType-printable photodetector arrays for multichannel meta-infrared imaging
Guo, J., Gu, S., Lin, L., Liu, Y., Cai, J., Cai, H., Tian, Y. P., Zhang, Y., Zhang, Q., Liu, Z., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-49592-4 · Cited by 54
“However, achieving high-quality patterned graphene remains a challenge because of the inevitable edge disorder and amorphization that occur during the patterning process using standard techniques such aschemical vapor deposition (CVD) growth, lithography, and ion/chemical etching 16 – 18 .”
MethodAccuracy and Political Bias of News Source Credibility Ratings by Large Language Models
Yang, K.-C., & Menczer, F. (2025) · Proceedings of the 17th ACM Web Science Conference 2025 · doi:10.1145/3717867.3717903 · Cited by 81
“Our study follows a long line of scholarship aimed at understanding how online platforms affect user information diets (Rahwan et al. 2019) and their contributions to issues such as the proliferation of misinformation (Lazer et al. 2018), echo chambers (Cinelli et al. 2021), and polarization…”
Abramson, C. I., & Levin, M. G. (2021) · Communicative & Integrative Biology · doi:10.1080/19420889.2021.2005863 · Cited by 55
Making Everyday Things Talk: Speculative Conversations into the Future of Voice Interfaces at Home
Reddy, A. V., Kocaballi, A. B., Nicenboim, I. P., Søndergaard, M. L. J., Lupetti, M. L., Key, C., Speed, C., Lockton, D., Giaccardi, E., Grommé, F., et al. (2021) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3411763.3450390 · Cited by 50
Dual-contrast pedagogy for AI literacy in upper elementary schools
Dai, Y. (2024) · Learning and Instruction · doi:10.1016/j.learninstruc.2024.101899 · Cited by 60
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.
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