All posts by : Kiona Bijker

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.

References

Allyn, B. (November 2025). Here's why concerns about an AI bubble are bigger than ever. Published online at NPR. Retrieved from: https://www.npr.org/2025/11/23/nx-s1-5615410/ai-bubble-nvidia-openai-revenue-bust-data-centers

Brown, N. C., Marinus, E., & Hubbard Cheuoua, A. (2022, August). Launching registered report replications in computer science education research. In Proceedings of the 2022 ACM Conference on International Computing Education Research, Volume 1, 309-322.

Carvão, P. (August 2025). Is The AI Bubble Bursting? Lessons From The Dot-Com Era. Published online at Forbes.Retrieved from: https://www.forbes.com/sites/paulocarvao/2025/08/21/is-the-ai-bubble-bursting-lessons-from-the-dot-com-era/

Casselman, B. & Ember, S. (November 2025). The A.I. Boom Is Driving the Economy. What Happens if It Falters? Published online at NY Times. Retrieved from: https://www.nytimes.com/2025/11/22/business/the-ai-boom-economy.html

Cockburn, A., Dragicevic, P., Besançon, L., & Gutwin, C. (2020). Threats of a replication crisis in empirical computer science. Communications of the ACM, 63(8), 70-79.

Giattino, C., Mathieu, E., Samborska, V., & Roser, M. (2023) Artificial Intelligence Published online at OurWorldinData.org. Retrieved from: 'https://ourworldindata.org/artificial-intelligence'

Gundersen, O.E., Cappelen, O., Mølnå, M. and Nilsen, N.G. (2025). The Unreasonable Effectiveness of Open Science in AI: A Replication Study. Proceedings of the AAAI Conference on Artificial Intelligence. 39, 25 (Apr. 2025), 26211-26219. DOI:https://doi.org/10.1609/aaai.v39i25.34818.

Miłkowski, M., Hensel, W. M., & Hohol, M. (2018). Replicability or reproducibility? On the replication crisis in computational neuroscience and sharing only relevant detail. Journal of computational neuroscience, 45(3), 163-172.

Sheeks, M. (2023). The Myth of the Good Epistemic Bubble. Episteme, 20(3), 685–700. https://doi.org/10.1017/epi.2022.52

Nguyen, C. Thi. (2020). Echo Chambers and Epistemic Bubbles. Episteme 17 (2): 141–61.https://doi.org/10.1017/epi.2018.32.

Yip J. (October 2025) Are we in an AI bubble? Here’s what analysts and experts are saying Published online at cnbc.com. Retrieved from: https://www.cnbc.com/2025/10/21/are-we-in-an-ai-bubble.html

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?

Submission timeline

Organising Committee

• Y. J. Erden (University of Twente)
• Kiona Bijker (University of Twente)
• Katleen Gabriels (Maastricht University)
• Martin Lentschat (Université Toulouse)
• Doina Bucur (University of Twente)

Programme committee 

• Maren Behrensen (Philosophy, University of Twente)
• Marcus Gerhold (Computer Science, University of Twente)
• Susannah E. Glickman (History, Stony Brook University)
• Adam Henschke (Philosophy, University of Twente)
• Saana Jukola (Philosophy, University of Twente)
• Miles MacLeod (Philosophy, University of Twente)
• Cyrus C. M. Mody (STS, Maastricht University)
• Yagmur Ozturk (Grenoble Informatics Laboratory (LIG), Université Grenoble Alpes)
• Stephen Rainey (Philosophy, TU Delft)
• Danielle Shanley (Philosophy, Maastricht Univertisy)
• Nicola Strisciuglio (Computer Science, University of Twente)
• Rob Wortham (Dept of Electronic and Electrical Engineering, University of Bath)

Schedule

The schedule will appear here when it becomes available

Call for Abstracts

Please send any questions to Y. J. Erden (University of Twente):

1-2 July 2026

AISB 2026, University of Sussex, UK, https://aisb.org.uk/

Keynote Speaker: Anil Seth, Professor of Cognitive and Computational Neuroscience, University of Sussex

Day of celebration: life and work of Prof Margaret Boden, 30 June. 

Attendance at the day of celebration is free for attendees of the AISB Convention. 

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. 

Symposium website: https://aisb.org.uk/aisb-2026-symposium-hype-promise-and-speculation

Submission: Extended abstracts of 500 words (maximum, excluding references) to Easychair: https://easychair.org/conferences/?conf=aibc2026

Deadline: 23 March 2026

OVERVIEW: 

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 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 2020). 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. Epistemic 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.

TOPICS OF INTEREST 

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?

SUBMISSION AND PUBLICATION DETAILS 

Submission: Extended abstracts of 500 words (maximum, excluding references) to Easychair: https://easychair.org/conferences/?conf=aibc2026

Deadlines: 

• Abstract submission deadline: 23 March 2026
• Notification of acceptance/rejection decisions: 17 April 2026
• Final versions of accepted abstracts: 22 May 2026
• Conference: 1 to 2 July 2026 [symposium date tbc] 


SYMPOSIUM ORGANISERS: 

Organising Committee

• Y. J. Erden (University of Twente) y.j.erden@utwente.nl
• Kiona Bijker (University of Twente) k.bijker@student.utwente.nl
• Katleen Gabriels (Maastricht University) k.gabriels@maastrichtuniversity.nl
• Martin Lentschat (Université Toulouse) martin.lentschat@univ-tlse2.fr 
• Doina Bucur (University of Twente) d.bucur@utwente.nl

Programme committee 

• Maren Behrensen (Philosophy, University of Twente)
• Marcus Gerhold (Computer Science, University of Twente)
• Susannah E. Glickman (History, Stony Brook University)
• Adam Henschke (Philosophy, University of Twente)
• Saana Jukola (Philosophy, University of Twente)
• Miles MacLeod (Philosophy, University of Twente)
• Cyrus C. M. Mody (STS, Maastricht University)
• Yagmur Ozturk (Grenoble Informatics Laboratory (LIG), Université Grenoble Alpes)
• Stephen Rainey (Philosophy, TU Delft)
• Danielle Shanley (Philosophy, Maastricht Univertisy)
• Nicola Strisciuglio (Computer Science, University of Twente)
• Rob Wortham (Dept of Electronic and Electrical Engineering, University of Bath)

About the AISB: https://aisb.org.uk/

The Society for the Study of Artificial Intelligence and Simulation of Behaviour (AISB) is the largest Artificial Intelligence Society in the United Kingdom. Founded in 1964, the society has an international membership from academia and industry, with a serious interest in Artificial Intelligence, Cognitive Science and related areas. It is a member of the European Coordinating Committee for Artificial Intelligence. The AISB Convention typically consists of a set of co-located symposia on a wide-range of topics in AI and the simulation of behaviour; there are often also a number of plenary lectures, and other events such as public engagement sessions, and historical/artistic exhibitions. The symposium model allows for the community to decide what the current topics of interest are and the direction that the field is heading. The event is central to the AISB and its mandate of promoting AI research, and in providing early career researchers and students a supportive environment in which to discuss their research. 

References

Allyn, B. (November 2025). Here's why concerns about an AI bubble are bigger than ever. Published online at NPR. Retrieved from: https://www.npr.org/2025/11/23/nx-s1-5615410/ai-bubble-nvidia-openai-revenue-bust-data-centers

Brown, N. C., Marinus, E., & Hubbard Cheuoua, A. (2022, August). Launching registered report replications in computer science education research. In Proceedings of the 2022 ACM Conference on International Computing Education Research, Volume 1, 309-322.

Carvão, P. (August 2025). Is The AI Bubble Bursting? Lessons From The Dot-Com Era. Published online at Forbes.Retrieved from: https://www.forbes.com/sites/paulocarvao/2025/08/21/is-the-ai-bubble-bursting-lessons-from-the-dot-com-era/

Casselman, B. & Ember, S. (November 2025). The A.I. Boom Is Driving the Economy. What Happens if It Falters? Published online at NY Times. Retrieved from: https://www.nytimes.com/2025/11/22/business/the-ai-boom-economy.html

Cockburn, A., Dragicevic, P., Besançon, L., & Gutwin, C. (2020). Threats of a replication crisis in empirical computer science. Communications of the ACM, 63(8), 70-79.

Giattino, C., Mathieu, E., Samborska, V., & Roser, M. (2023) Artificial Intelligence Published online at OurWorldinData.org. Retrieved from: 'https://ourworldindata.org/artificial-intelligence'

Gundersen, O.E., Cappelen, O., Mølnå, M. and Nilsen, N.G. 2025. The Unreasonable Effectiveness of Open Science in AI: A Replication Study. Proceedings of the AAAI Conference on Artificial Intelligence. 39, 25 (Apr. 2025), 26211-26219. DOI:https://doi.org/10.1609/aaai.v39i25.34818.

Miłkowski, M., Hensel, W. M., & Hohol, M. (2018). Replicability or reproducibility? On the replication crisis in computational neuroscience and sharing only relevant detail. Journal of computational neuroscience, 45(3), 163-172.

Sheeks, M. (2023). The Myth of the Good Epistemic Bubble. Episteme, 20(3), 685–700. https://doi.org/10.1017/epi.2022.52

Nguyen, C. Thi. 2020. “ECHO CHAMBERS AND EPISTEMIC BUBBLES.” Episteme 17 (2): 141–61.https://doi.org/10.1017/epi.2018.32.

Yip J. (October 2025) Are we in an AI bubble? Here’s what analysts and experts are saying Published online at cnbc.com. Retrieved from: https://www.cnbc.com/2025/10/21/are-we-in-an-ai-bubble.html