UKRI Centre for Doctoral Training in Safe and Trusted Artificial Intelligence

Lead Research Organisation: King's College London
Department Name: Informatics


The UK is world leading in Artificial Intelligence (AI) and a 2017 government report estimated that AI technologies could add £630 billion to the UK economy by 2035. However, we have seen increasing concern about the potential dangers of AI, and global recognition of the need for safe and trusted AI systems. Indeed, the latest UK Industrial Strategy recognises that there is a shortage of highly-skilled individuals in the workforce that can harness AI technologies and realise the full potential of AI.

The UKRI Centre for Doctoral Training (CDT) on Safe and Trusted AI will train a new generation of scientists and engineers who are experts in model-based AI approaches and their use in developing AI systems that are safe (meaning we can provide guarantees about their behaviour) and are trusted (meaning we can have confidence in the decisions they make and their reasons for making them). Techniques in AI can be broadly divided into data-driven and model-based. While data-driven techniques (such as machine learning) use data to learn patterns or behaviours, or to make predictions, model-based approaches use explicit models to represent and reason about knowledge. Model-based AI is thus particularly well-suited to ensuring safety and trust: models provide a shared vocabulary on which to base understanding; models can be verified, and solutions based on models can be guaranteed to be correct and safe; models can be used to enhance decision-making transparency by providing human-understandable explanations; and models allow user collaboration and interaction with AI systems. In sophisticated applications, the outputs of data-driven AI may be input to further model-driven reasoning; for example, a self-driving car might use data-driven techniques to identify a busy roundabout, and then use an explicit model of how people behave on the road to reason about the actions it should take.

While much current attention is focussed on recent advancements in data-driven AI, such as those from deep learning, it is crucial that we also develop the UK skills base in complementary model-based approaches to AI, which are needed for the development of safe and trusted AI systems. The scientists and engineers trained by the CDT will be experts in a range of model-based AI techniques, the synergies between them, their use in ensuring safe and trusted AI, and their integration with data-driven approaches. Importantly, because AI is increasingly pervasive in all spheres of human activity, and may increasingly be tied to regulation and legislation, the next generation of AI researchers must not only be experts on core AI technologies, but must also be able to consider the wider implications of AI on society, its impact on industry, and the relevance of safe and trusted AI to legislation and regulation. Core technical training will be complemented with skills and knowledge needed to appreciate the implications of AI (including Social Science, Law and Philosophy) and to expose them to diverse application domains (such as Telecommunications and Security). Students will be trained in responsible research and innovation methods, and will engage with the public throughout their training, to help ensure the societal relevance of their research. Entrepreneurship training will help them to maximise the impact of their work and the CDT will work with a range of industrial partners, from both the private and public sectors, to ensure relevance with industry and application domains and to expose our students to multiple perspectives, techniques, applications and challenges.

This CDT is ideally equipped to deliver this vision. King's and Imperial are each renowned for their expertise in model-driven AI and provide one of the largest groupings of model-based AI researchers in the UK, with some of the world's leaders in this area. This is complemented with expertise in technical-related areas and in the applications and implications of AI.

Planned Impact

The UKRI Centre for Doctoral Training in Safe and Trusted Artificial Intelligence (AI) will impact on four primary beneficiary groups: the students trained by the CDT; the UK AI research and development community working on AI systems that are safe and trusted; the general public, whose daily lives are affected by AI systems and the decisions they make; and the private and public sectors, who use AI in a multitude of ways, including in decision making and service provision.

Impact on students
The CDT will train a minimum of 70 students to develop safe and trusted AI systems. They will be equipped with technical skills in model-based AI techniques (needed for safe and trusted AI), the synergies between these techniques and their integration with data-driven AI approaches. Crucially, they will be trained to consider the implications of AI, with multi-disciplinary skills and knowledge necessary for responsible research and innovation of AI, including ethics, law, public engagement and value-sensitive design. Students will gain exposure to a breadth of different challenges, implications and applications of AI, as well as gaining group working and entrepreneurship skills. Through engagement with industrial partners (including internships, co-supervision, training, and hackathons) students will be exposed to real-world challenges and ways of working, increasing their employability. Finally, students will develop key transferable and leadership skills, through targeted training and through experience supporting the management of the CDT (e.g., event organisation etc.). Students outside of the CDT will also benefit through attendance at activities such as summer schools, hackathons and conferences.

Impact on UK AI research and development community
The new generation of scientists and engineers developed by the CDT will ensure that the UK maintains its expertise in model-driven AI, complementary to the current focus on data-driven approaches. These experts, also trained to consider the societal implications of AI, will lead research and development in safe and trusted AI systems, ensuring that the UK remains at the forefront of AI research and realises the potential benefits of AI. Through multi-disciplinary engagement, for example at hackathons and through co-supervision of PhD, we expect the CDT also to foster research on the implications of AI.

Impact on public
Through the CDT's focus on using model-based approaches to allow human users to engage with and understand AI decision-making, and to provide formal guarantees of AI system behaviours, we expect to increase societal trust in AI. By taking seriously the implications of AI, the students trained by the CDT will be focussed on achieving societal benefit of AI systems. A series of innovative public engagement activities will both positively impact on societal trust in AI and will allow the public to input into the CDT research and help ensure its benefits to society. The CDT will draw on its connections with Science Gallery London and Imperial's Science Festival to ensure effectiveness and reach of our planned public engagement events.

Impact on private and public sectors
The UK is facing an AI skills shortage and the private and public sectors will profit from the injection into the workforce of AI experts, trained in responsible research and innovation of safe and trusted AI. Our partners (to date, we have secured interest and support from 22 organisations) will gain direct access to the CDT students, who will be trained to be the future leaders in safe and trusted AI. Importantly, through co-creation and review of the CDT training programme, and through engagement with training and events, our partners will be able to influence the skills developed by the students, ensuring they are ideally equipped to meet the needs of the private and public sectors. By engaging with the thriving London incubator community, we will also account for the needs of tech start-up


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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/S023356/1 31/03/2019 29/09/2027
2268984 Studentship EP/S023356/1 30/09/2019 29/09/2023 Patrik Henrikson
2268879 Studentship EP/S023356/1 30/09/2019 29/09/2023 Sophia KALANOVSKA
2268904 Studentship EP/S023356/1 30/09/2019 31/01/2024 Nandi Schoots
2268782 Studentship EP/S023356/1 30/09/2019 29/09/2024 Alex Jackson
2268746 Studentship EP/S023356/1 30/09/2019 29/04/2024 Dylan Cope
2268917 Studentship EP/S023356/1 30/09/2019 30/11/2023 Hana Kopecka
2268793 Studentship EP/S023356/1 30/09/2019 31/03/2024 Joseph Pober
2268895 Studentship EP/S023356/1 30/09/2019 21/12/2023 Charles Higgins
2268857 Studentship EP/S023356/1 30/09/2019 29/09/2023 Jazon Szabo
2269025 Studentship EP/S023356/1 30/09/2019 29/09/2023 Aamal Hussain
2401186 Studentship EP/S023356/1 30/09/2020 29/09/2024 Munkhtulga Battogtokh
2400643 Studentship EP/S023356/1 30/09/2020 29/09/2024 Benjamin Batten
2401115 Studentship EP/S023356/1 30/09/2020 29/09/2025 Samuel Martin
2642123 Studentship EP/S023356/1 30/09/2020 31/12/2025 Alexander Gaskell
2401202 Studentship EP/S023356/1 30/09/2020 21/06/2021 Sean Baccas
2401042 Studentship EP/S023356/1 30/09/2020 13/12/2024 Anna Gausen
2400610 Studentship EP/S023356/1 30/09/2020 29/09/2024 Fabrizio Russo
2401206 Studentship EP/S023356/1 30/09/2020 12/02/2021 Lara Dal Molin
2400921 Studentship EP/S023356/1 30/09/2020 30/12/2024 Francis Rhys Ward
2401113 Studentship EP/S023356/1 30/09/2020 29/09/2024 Mackenzie Jorgensen
2401214 Studentship EP/S023356/1 30/09/2020 01/04/2025 Mattia Kelly Villani
2605850 Studentship EP/S023356/1 30/09/2021 29/09/2025 Madeleine Waller
2605881 Studentship EP/S023356/1 30/09/2021 29/09/2025 Luke Thorburn
2605862 Studentship EP/S023356/1 30/09/2021 09/12/2025 Matthew Shorvon
2605989 Studentship EP/S023356/1 30/09/2021 29/09/2025 Elfia Bezou Vrakatseli
2605901 Studentship EP/S023356/1 30/09/2021 29/09/2025 Lennart Wachowiak
2606004 Studentship EP/S023356/1 30/09/2021 29/09/2025 Shahin Honarvar
2605689 Studentship EP/S023356/1 30/09/2021 29/09/2025 Ruud Skipper
2606058 Studentship EP/S023356/1 30/09/2021 29/03/2026 Matthew MacDermott
2605657 Studentship EP/S023356/1 30/09/2021 29/09/2025 Amir Kiani
2606055 Studentship EP/S023356/1 30/09/2021 29/09/2025 Avinash Gurushiddappa Kori
2605998 Studentship EP/S023356/1 30/09/2021 29/09/2025 Benedikt Brueckner
2605778 Studentship EP/S023356/1 30/09/2021 29/09/2025 Peter Tisnikar
2751802 Studentship EP/S023356/1 30/09/2022 29/09/2026 Gabriele La Malfa
2742237 Studentship EP/S023356/1 30/09/2022 29/09/2026 Usman Islam
2751925 Studentship EP/S023356/1 30/09/2022 29/09/2026 Alexander Rader
2751991 Studentship EP/S023356/1 30/09/2022 29/09/2026 Maria Stoica
2751767 Studentship EP/S023356/1 30/09/2022 29/09/2026 Zoe Evans
2752143 Studentship EP/S023356/1 30/09/2022 29/09/2026 Michelle Nwachukwu
2741847 Studentship EP/S023356/1 30/09/2022 29/09/2026 Alexander Goodall
2751885 Studentship EP/S023356/1 30/09/2022 29/09/2030 Mariya Pavlova
2741829 Studentship EP/S023356/1 30/09/2022 29/09/2026 Tiberiu Georgescu
2754642 Studentship EP/S023356/1 30/09/2022 29/09/2026 Nathan Schneider Gavenski
2741841 Studentship EP/S023356/1 30/09/2022 29/09/2026 Chiara Di Bonaventura
2751971 Studentship EP/S023356/1 30/09/2022 29/09/2026 Stefan Roesch
2752071 Studentship EP/S023356/1 01/12/2022 30/11/2026 Xinyi Ye
2752049 Studentship EP/S023356/1 01/12/2022 30/11/2026 Wenxi Wu