Evidence-based policy, behavioural economics, healthcare, well-being, statistics, machine learning, AI, and human-AI teaming
Dr Tomas Folke joined the CBR in 2018 after finishing his PhD in Psychology from the University of Cambridge. His PhD topic was the role of confidence in perceptual and value-based decision-making. During this time he was the team leader for the Neuroscience and Cognition branch of the Policy Research Group’s Junior Researcher programme and taught Bayesian statistics as a teaching assistant at the Essex Summer School in Social Science Data Analysis. Prior to his PhD he completed an MPhil in Social and Developmental Psychology (Distinction) at the University of Cambridge and a BSc in Psychology at the University of York.
His current research focuses on applying behavioural science to improve human-AI collaborations by making AI more understandable to humans and humans more understandable to AI. He works with a multidisciplinary team of mathematicians, computer scientists and cognitive scientists on projects related to artificial social intelligence and AI explainability. Tomas is also interested in quantitative methods in the behavioural sciences, specifically the relationship between research design and analysis methods, the application and misapplication of statistical modelling, and the potential of big data and machine learning to provide novel behavioural insights.
Folke T., Li Z., Sojitra, R.B., Yang S.C.H., & Shafto P. (2021). Explainable AI for Natural Adversarial Images. ICLR workshop for Responsible AI.
Folke T., Yang S.C.H., Anderson S., & Shafto P. (2021). Explainable AI for medical imaging: explaining pneumothorax diagnoses with Bayesian teaching. SPIE Defense and Commercial Sensing Conference.
Yang, S. C. H., Vong, V.K., Sojitra, R.B., Folke, T., & Shafto, P. (2021). Mitigating belief projection in explainable artificial intelligence via Bayesian Teaching. Scientific Reports.
Yang, S. C. H., Folke, T., & Shafto P. (In press). Abstraction, generalization and reuse for Explainable AI. Applied AI letters.
Ruggeri, K., Većkalov, B., Bojanić, L., Andersen, T. L., Ashcroft-Jones, S., Ayacaxli, N., … & Folke, T. (2021). The general fault in our fault lines. Nature Human Behaviour, 1-11.
Ruggeri, K., Alí, S., Berge, M. L., Bertoldo, G., Bjørndal, L. D., Cortijos-Bernabeu, A., Davison, C., Demić, E., Esteban-Serna, C., Friedemann, M., Gibson, S. P., Jarke, H., Karakasheva, R., Khorrami, P. R., Kveder, J., Andersen, T. L., Lofthus, I. S., McGill, L., Nieto, A. E., … Folke, T. (2020). Replicating patterns of prospect theory for decision under risk. Nature Human Behaviour.
Ruggeri, K., Benzerga, A., Verra, S., & Folke, T. (2020). A behavioral approach to personalizing public health. Behavioural Public Policy, 1–13.
Ruggeri, K., Folke, T., Jarke, H., Paul, A., F., Gladstone, J. (2018). Economic, financial and consumer decision-making. In K. Ruggeri (Ed.), Behavioural insights for policy: Concepts and cases: (pp. 156-179). Routledge.
Ruggeri, K., Ojinaga-Alfageme, O., Benzarga, A., Berkessel, J., Hlavová, R., Kunz, M., Pohl, N., Sundström, F., Folke, T. (2018). Evidence-based policy. In K. Ruggeri (Ed.), Behavioural insights for policy: Concepts and cases: (pp. 39-80). Routledge.
Folke, T., Jacobsen, C., Fleming, S. M., & De Martino, B. (2017). Explicit representation of confidence informs future value-based decisions. Nature Human Behaviour, 1(1), 0002.
Folke, T., Ouzia, J., Bright, P., De Martino, B., & Filippi, R. (2016). A bilingual disadvantage in metacognitive processing. Cognition, 150, 119-132.