Following a committee decision to review membership fees and categories, international members now benefit from the same rates as UK/EU members. We have also introduced a new three-year discounted student/unwaged/retired rate. Membership rates were last increased in 2018. Since then, we have all experienced significant inflation, and AISB’s administrative costs have increased significantly. EurAI will be increasing their affiliation fee by 33% in 2026. In the UK the cumulative inflation since 2018 has been almost 29%. Whilst we recognise that academic members’ salaries may not have kept pace with inflation, we feel it is essential to increase membership fees to help address increased costs. The new Ordinary Member rate of £55 represents an increase of just under 15%. The new rates are available on the web site: https://aisb.org.uk/membership-options/
AISB 2026 Symposium: Hype, Promise, and Speculation: AI Bubbles and the Replication Crisis in Computer Science
AISB convention information
1-2 July 2026
AISB 2026, University of Sussex, 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 to 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 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
March 23 2026
Submission of extended abstracts
March 30 2026
Abstracts allocated to reviewers
April 17 2026
Deadline for reviews, for circulation to authors
May 22 2026
Date by which camera-ready copies of final abstracts 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.
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
Michael Faraday Prize Lecture: This is not the AI we were promised
Talk Announcement: Professor Michael Wooldridge, AISB Fellow
- 18 February 2026
- 18:30 – 19:30
- The Royal Society
- Watch online
Contemporary AI systems such as ChatGPT seem to offer articulate, wide-ranging expertise — yet beneath the surface, they fail many basic tests of rational intelligence. In this engaging talk, Professor Michael Wooldridge (Fellow of AISB) explores how these systems actually work and why they display such strange, inconsistent, and often entertaining behaviour. He will contrast today’s AI with classical ideas of logic and reason, and discuss what these developments mean for the future frontiers of artificial intelligence — and for the enduring dream of truly intelligent machines.
Full details are available here on the Royal Society web site.
Member discounts on DELL equipment
We are delighted to announce that AISB members can now receive discounts on DELL computer equipment and accessories. Simply log in to the AISB web site, access the ‘Members Area’ menu and select the ‘Member benefits from DELL’ menu item for full instructions.
AISB 2026 – Call for Symposia Proposals
CALL FOR SYMPOSIA PROPOSALS: AISB 2026, University of Sussex
DEADLINE: November 30, 2025Contact: Simon Bowes S.C.Bowes@sussex.ac.uk
AISB 2026 will be held at the University of Sussex on the 1st-2nd July. Further information on arrangements for the convention will be made available as information becomes available.
Keynote Speaker: Anil Seth
The AISB 2026 convention will follow the same overall structure as previous conventions, namely a set of co-located symposia, and we are seeking proposals for these symposia. Typical symposia last for one or two days, and can include any type of event of academic benefit: talks, posters, panels, discussions, demonstrations, outreach sessions, etc. Proposals for Symposia are welcomed in all areas of AI and cognitive science. Some suggested areas are shown below, although any proposal in the field of AI or cognitive science will be welcomed:
- AI in Education
- Agency & AI
- Art & AI
- Cognitive & Computational Neuroscience
- Computational theory of mind
- Computational Intelligence
- Consciousness
- Embodiment and AI
- Ethics of AI
- Human and Machine Creativity
- Hybrid Human-AI
- Knowledge Representation
- Machine Learning
- Robotics
- Bio-inspired approaches
- Simulation of Human and Animal Behaviour
- The Turing Test and Philosophical Foundations of AI
Proposing a Symposium
Each symposium is organized by its own programme committee. The committee proposes the symposium, defines the area(s) and structure for it, issues calls for abstracts/papers etc., manages the process of selecting submitted papers for inclusion, and compiles an electronic file for inclusion in the convention proceedings.
Proposers are welcome to submit or be involved with more than one proposal. Proposers need not already be members the AISB and will not be required to become members. They will of course be encouraged to join!
Deadline for symposium proposals: 30th November 2025Notification of acceptance: 15th December 2025
Submissions should consist of the following:
- A title.
- A 300–1000-word description of the scope of the symposium, and its relevance to the convention along with the nature of the academic events (talks, posters, panels, demonstrations, etc.).
- Whether the symposium is intended as a sequel to a symposium at a previous AISB conference.
- An indication of whether submissions will be by abstract, extended abstract, or full paper.
- Your preferences about the intended length of the symposium as a number of days (half a day, one day or two days), together with a brief justification.
- A description (up to 500 words) of any experience you have in organization of academic research meetings (please note that it is not a requirement that you have such experience).
- Names and affiliations of any invited speakers that you may have in mind for the symposium.
- Your names and full contact details, together with, if possible, names and workplaces of the members of a preliminary, partial programme committee.
Michael Faraday Prize awarded to AISB Fellow Prof Mike Wooldridge
It is with great joy that we note this week that the Royal Society has awarded the Michael Faraday Prize and Lecture 2025 to AISB Fellow Professor Michael Wooldridge.
The award was made based on Professor Wooldridge’s award-winning work as a leading researcher, educator and commentator in the field of Artificial Intelligence (AI). His popular science books, lectures and media appearances have informed millions. Full details are available on the Royal Society web site.
AISB 2026 pre-announcement
We are happy to announce that the AISB 2026 Convention will be held at Sussex University in June 2026. More details and dates to follow shortly.
GOV.UK Rapid Technology Assessment
In 2024 AISB committee member Dr. Swen Gaudl was participating in an analysis made the Government Office for Science due to his involvement as CTO of a robotics company as well as his academic work. The key mission of GO Science is to provide material for decision making. Dr Gaudl’s involvement in the reports was through an extensive interview and feedback on the draft documents.
The resulting report aims to support government officials to better understand the need for further development in specific domains such as Humanoids or General Robotics and how the UK can remain or become a key global player. The key challenges Dr. Gaudl identified for keeping competitive were focused on small companies or start-ups and the support needs for growth based on his his experience as founder, former lead developer & CTO for konpanion as well requirements for high profile personal.
The reports were released in Spring 2025.
The two reports Dr Gaudl advised on:
AI & Big Data Expo at TechEx North America
AISB is media partner with the AI & Big Data Expo, with regular presence at their London event. AI & Big Data Expo North America, a key part of TechEx North America, is the premier event showcasing Generative AI, Enterprise AI, Machine Learning, Security, Ethical AI, Deep Learning, Data Ecosystems, and NLP. Register for your free or gold pass to attend.
AISB/AIxIA Spotlight Seminar on AI – Alessio Lomuscio
Title: Towards Verification of Neural Systems.
Speaker: Alessio Lomuscio, PhD.
Imperial College London.
Safe Intelligence.
21 March, 5pm CET (4pm GMT)
Abstract:
A major challenge in deploying ML-based systems, such as ML-based computer vision, is the inherent difficulty in ensuring their performance in the operational design domain. The standard approach consists in extensively testing models against a wide collection of inputs. However, testing is inherently limited in coverage, and it is expensive in several domains.
Novel verification methods provide guarantees that a neural model meets its specifications in dense neighbourhood of selected inputs. For example, by using verification methods we can establish whether a model is robust with respect to infinitely many re-illumination changes, or particular noise patterns in the vicinity to an input. Verification methods can also be tailored to specifications in the latent space and establish the robustness of models against semantic perturbations not definable in the input space (3D pose changes, background changes, etc). Additionally, verification methods can be paired with learning to obtain robust learning methods capable of generating models inherently more robust than those that may be derived with standard methods.
In this presentation I will succinctly cover the key theoretical results leading to some of the present ML verification technology, illustrate the resulting toolsets and capabilities, and describe some of the use cases developed with our colleagues at Boeing Research, including centerline distance estimation, object detection, and runway detection.
I will argue that verification and robust learning can be used to obtain models that are inherently more robust than present learning and testing approaches, thereby unlocking the deployment of applications in society critical capplications.
Bio:
Alessio Lomuscio is Professor of Safe Artificial Intelligence at Imperial College London (UK), where he leads the Safe AI Lab. He is a Distinguished ACM member, a Fellow of the European Association of Artificial Intelligence and currently holds a Royal Academy of Engineering Chair in Emerging Technologies. He is founding co-director of the UKRI Doctoral Training Centre in Safe and Trusted Artificial Intelligence.
Alessio’s research interests concern the development of verification methods for artificial intelligence. Since 2000 he has pioneered the development of formal methods for the verification of autonomous systems and multi-agent systems, both symbolic and ML-based. He has published over 200 papers in leading AI and formal methods conferences and journals.
He is the founder and CEO of Safe Intelligence, a VC-backed Imperial College London spinout helping users build and assure robust ML systems.