AISB 2026 Symposium | Hype, Promise, and Speculation: AI Bubbles and the Replication Crisis in Computer Science

Symposium outline

In this symposium we intend to tackle complementary issues related to the likelihood of a replication crisis in computer science and computational methods, and an emerging AI bubble on the other.

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” (Gundersen et al 2025, Cockburn et al 2020). In this symposium we look at the replication crisis as it pertains especially to computer science, whether within the discipline (cf. Cockburn et al 2020), or as applied to, or utilised in, other disciplines, such as computational modelling for neuroscience (Miłkowski et al 2018).

There is also uncertainty about the extent to which ‘questionable research practices’ (QRPs) can be found in the above contexts. These can include manipulating data for statistically significant results (p-hacking), post hoc analysis to find statistically significant outcomes (p-fishing), or so as to present these as expected, i.e. ‘Hypothesising After the Results are Known’ (HARKing) (Cockburn et al 2020). Meanwhile, there are also proposals to address QRPs in computer science research, for instance through replication or the use of pre-study registered reports that include hypotheses and methods etc (Brown et al 2022).

AI bubbles

It’s clear that AI development is expanding substantially (Giattino et al 2023) , 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 (Carvão 2025). 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. The economic damage of a collapse is already estimated by US commentators to rival the bursting of the dot-com bubble in 1990 and the financial crash of 2008 (Allyn 2025, Casselman and Ember 2025, Yip 2025). In the symposium we look beyond the speculation of AI stocks at the promises and reality of AI capabilities and what the effects of the potential bubble are.

In addition to the above are epistemic bubbles, which form around new or ‘popular’ ideas. ‘Epistemic bubbles’ may include ‘self-segregated’ networks of ‘like-minded people’ whose members are ‘liable to converge on and resist correction of false, misleading or unsupported claims’ (Sheeks 2023). These bubbles can in turn create ‘social epistemic’ structures which are similar to echo chambers, ‘in which other relevant voices have been actively discredited’ (Nguyen). In AI contexts, these epistemic bubbles might exclude voices who are critical of these technologies, or who doubt either its identity as AI, or its scope for positive impacts and change. Not least as ‘AI’ as a term brings greater expectations, including financial, compared with describing the technology in terms of its components and capacities, e.g. as LLMs, RAGs, DNNs, transformers, models, etc. Bubbles can also be created through the use of AI itself, for instance due to its scope for personalisation on media platforms, and agreeableness in GenAI chatbots, such that views of users are neither challenged nor developed.

Submissions

We invite papers from a wide range of disciplines, including computer science, AI, Machine Learning, Natural Language Processing, Explainable AI, philosophy, behavioural sciences, psychology, social sciences, and those working with computational models, e.g. in finance.

We welcome a broad variety of topics, including but not limited to:

• Machine learning (e.g. modelling, AI)
• Large language models
• Neural networks
• Deep learning
• Explainable AI
• Decision trees
• Replication crisis
• AI bubble(s)

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?

Important dates:

• March 6 2026: Submission of extended abstracts
• March 30 2026: Abstracts allocated to viewers
• April 17 2026: Deadline for reviews, for circulation to authors
• May 22 2026: Date by which camera-ready copies of final papers should be received from authors, along with completed copyright forms.
• June 5 2026: PDF Camera ready proceedings submitted to AISB-2026 organisers, along with all copyright forms.

Source and more details: https://aisb.org.uk/aisb-2026-symposium-hype-promise-and-speculation/