[SIGCIS-Members] Call for Abstracts for AISB 2026 Symposium: Hype, Promise, and Speculation: AI Bubbles and the Replication Crisis in Computer Science

[Susannah Glickman]

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 at utwente.nl <mailto:y.j.erden at 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 at utwente.nl <mailto:y.j.erden at utwente.nl>
Kiona Bijker (University of Twente) k.bijker at student.utwente.nl <mailto:k.bijker at student.utwente.nl>
Katleen Gabriels (Maastricht University) k.gabriels at maastrichtuniversity.nl <mailto:k.gabriels at maastrichtuniversity.nl>
Martin Lentschat (Université Toulouse) martin.lentschat at univ-tlse2.fr <mailto:martin.lentschat at univ-tlse2.fr> 
Doina Bucur (University of Twente) d.bucur at utwente.nl <mailto:d.bucur at utwente.nl>
  
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