Dear all, Hope you are enjoying the very beginnings of spring! I am on the program committee for this exciting upcoming symposium and wanted to pass along the details below. All the best, Susannah Call for Abstracts: AISB 2026 Symposium: Hype, Promise, and Speculation: AI Bubbles and the Replication Crisis in Computer Science 1-2 July 2026 AISB 2026, University of Sussex, UK, https://aisb.org.uk/ Keynote Speaker: Anil Seth, Prof of Cognitive & Computational Neuroscience Symposium: https://aisb.org.uk/aisb-2026-symposium-hype-promise-and-speculation <https://aisb.org.uk/aisb-2026-symposium-hype-promise-and-speculation/#Symposium_outline> Submission: Abstracts of 500 words Deadline: 23 March 2026 Please send any questions to Y. J. Erden (University of Twente): y.j.erden@utwente.nl <mailto:y.j.erden@utwente.nl> The replication crisis The replication crisis has crossed multiple fields in science asking if results presented in published papers can be reproduced, repeated, and/or replicated. In their efforts to verify results various disciplines, including computer science, have already found that the answer for too many papers is “no”. In this symposium we look at the replication crisis as it pertains especially to computer science, whether within the discipline, or as applied to, or utilised in, other disciplines, such as computational modelling for neuroscience. AI bubbles It’s clear that AI development is expanding substantially, but the extent to which this growth is sustainable is unclear. Meanwhile, the possibility of this becoming another bubble, like those from the dot com boom and real estate, is clear. A bubble is a vague concept that captures where a process or commodity is valued or hyped beyond its intrinsic worth, typically in unsustainable ways. If contemporary expectations currently dominating the AI field do turn out to be a bubble we can expect further expansion, and then collapse, typically causing damage in the process. We invite papers from a wide range of disciplines, including: computer science, AI, Machine Learning, Natural Language Processing, Explainable AI, philosophy, behavioural sciences, social sciences, and those working with computational models, e.g. in finance. Example research questions: What kinds of impacts are computational methods having on science, e.g. machine learning methods, statistical analysis? How do computer science methods harm or help the replicability of research? Is research in computer science replicable? Does the name ‘Artificial Intelligence’ have an effect on what is expected of AI? Are current valuations (financial, social etc) of AI realistic? Is there an AI bubble in science? Related bubbles that might be relevant to these topics, e.g. is big data also a bubble? Organising Committee Y. J. Erden (University of Twente) y.j.erden@utwente.nl <mailto:y.j.erden@utwente.nl> Kiona Bijker (University of Twente) k.bijker@student.utwente.nl <mailto:k.bijker@student.utwente.nl> Katleen Gabriels (Maastricht University) k.gabriels@maastrichtuniversity.nl <mailto:k.gabriels@maastrichtuniversity.nl> Martin Lentschat (Université Toulouse) martin.lentschat@univ-tlse2.fr <mailto:martin.lentschat@univ-tlse2.fr> Doina Bucur (University of Twente) d.bucur@utwente.nl <mailto:d.bucur@utwente.nl>