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Taha Yasseri

Workday Professor of Technology and Society

Taha Yasseri is the Workday Full Professor and Chair of Technology and Society at Trinity College Dublin and Technological University Dublin. He was a Professor and the Deputy Head at the School of Sociology and a Geary Fellow at the Geary Institute for Public Policy at University College Dublin, Ireland. Before that, he was a Senior Research Fellow in Computational Social Science at the University of Oxford, a Turing Fellow at the Alan Turing Institute for Data Science and Artificial Intelligence, and a Research Fellow in Humanities and Social Sciences at Wolfson College

Background

Taha Yasseri holds a PhD in Complex Systems Physics from the University of Göttingen, Germany. His research interests include the analysis of large-scale transactional data, behavioural experiments to understand human dynamics, the social behaviour of machines, government-society interactions, online political behaviour, mass collaboration, collective intelligence, information and opinion dynamics, hate speech and content moderation, collective behaviour, and online dating.

Social Behaviour of Machines
Artificial Intelligence (AI) is a major focus in academia, industry, and the public sector. While AI research has extensively examined various aspects of machine intelligence, the collective behaviour of machines has received less attention. Complex systems theory posits that the emergent behaviour of a system can differ significantly from the simple sum of its parts. In systems where multiple automated agents interact, such as Wikipedia's editing bots, this is particularly evident. Our research on these bots shows that despite sharing the goal of improving the encyclopaedia and having low intelligence, conflict and disorganization are common. This arises largely because these bots learn from humans, demonstrating that even simple automated systems can exhibit complex, unpredictable behaviours.

AI-Enabled Collective Intelligence
Collective Intelligence (CI) refers to the emergent outcome of collaborative efforts from many individuals, often resulting in intelligence superior to that of any single contributor. This concept was famously demonstrated in Galton’s 1906 "Wisdom of the Crowd" experiment and has since been applied successfully to solve problems that individuals or machines alone could not tackle. With the recent advancements in AI and its applications in decision-making and forecasting, it is crucial to explore how AI can enhance collective intelligence. AI can be instrumental in matching tasks to appropriate responders, training entire crowds, combining individual contributions, and translating these into collective decisions.

Key Publications

  • Cui, H., & Yasseri, T. (2024). AI-enhanced Collective Intelligence: The State of the Art and Prospects. Patterns (In Press)

  • Tsvetkova, M., Yasseri, T., Pescetelli, N., & Werner, T. (2024). Human-machine social systems. Nature Human Behaviour (In Press).

  • Burton, J. W., Lopez-Lopez, E., Hechtlinger, S., Rahwan, Z., Aeschbach, S., Bakker, M. A., ... & Hertwig, R. (2024). How large language models can reshape collective intelligence. Nature Human Behaviour (In Press).

  • Straub, V. J., Tsvetkova, M., & Yasseri, T. (2023). The cost of coordination can exceed the benefit of collaboration in performing complex tasks. Collective Intelligence, 2(2), 26339137231156912.

  • Breu, A., & Yasseri, T. (2023). What drives passion? An empirical examination on the impact of personality trait interactions and job environments on work passion. Current Psychology, 42(17), 14350-14367

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You can find more of Taha's publications here. 

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