UKRI Centre for Doctoral Training in Socially Intelligent Artificial Agents (SOCIAL)

Lead Research Organisation: University of Glasgow
Department Name: School of Computing Science



Social intelligence is an important aspect of human cognition making us capable of dealing with others' attitudes, intentions, feelings, personality, and expectations. Correspondingly, Artificial Social Intelligence is the area of Artificial Intelligence (AI) that aims at endowing machines with such social intelligence, i.e., with the ability to interact with their users in the same way as people interact with one another. While being driven by technological needs, Artificial Social Intelligence is an inherently interdisciplinary field that revolves around humans as much as it revolves around the building of machines.

As a result, the proposed Centre for Doctoral Training (CD) is based on the collaboration between different experts in human behaviour - students will be trained by specialists ranging in expertise from neural, physiological, cognitive and psychological processes to verbal/nonverbal societal communication - and experts in AI methodologies. They will gain expertise and skills that will range from the synthesis of human/societal interactive behaviour to the distillation of knowledge from sensors and data.

- Aims and objectives

The goal of the CDT is to train the next generation of experts in Artificial Social Intelligence, young researchers and practitioners well versed not only in AI, but also in a range of fields spanning from Psychology/Social Science and Neuroscience to Human-Computer Interaction and Data Science. These different disciplines will come together to train the cohort in:

a. Identifying principles and laws underlying social interactions between users and agents;
b. Developing technological approaches that allow artificial agents to act as believable partners in social interactions involving human users;
c. Integrating artificial agents into the wider technological infrastructures;
d. Investigating human responses to artificial agents in a naturalistic, real-world social settings.

Academic involvement will be in the form of the provision of courses across the topics listed above, advanced workshops and direct supervision in cutting edge research that is not necessarily (yet) part of the industrial workflow.

- Applications and Benefits

The proposed training approach will be in tight collaboration and co-creation with our industrial partners as the aim is to provide the students with the best of both the academic and industry worlds. Industrial involvement will be in the form of co-design and co-supervision of the PhD project as well as placements, usually over a period of 3 months. This will allow the students to co-create innovation through the PhD proposal and the development of specific, real-world industry problems. Such a tight interaction with industry will also be of advantage to the UK economy that will benefit from a pool of talent trained not only from a scientific and technological point of view, but also in terms of professional skills and experience necessary to operate in highly technological companies.

Students will further benefit from wider social sciences training through the proposed partnership with the Scottish Graduate School for Social Sciences (SGSSS), a ESRC funded Doctoral Training Partnership (DTP) with a track record for excellence in Teaching, Cohort training and Knowledge Exchange and Impact. The outlined training model will inform AI approaches with the findings on human behaviour and, vice versa, AI technologies will be used to better understand and model human behaviour.

Last, but not least, the emphasis on ethics and social issues is of great societal importance as AI-driven technologies play an increasingly important role in sensitive settings such as healthcare, assistance, education, law enforcement, etc.

Planned Impact

The main beneficiaries of the proposed CDT will be as follows:

1) The PhD students will benefit from a unique training program that will equip them with scientific and technological skills in Artificial Social Intelligence, while providing them with a wide spectrum of opportunities for enhancing their professional profile in terms of transferable skills (e.g., leadership, communication, ethics, Responsible Research Innovation, entrepreneurship, Intellectual Property management, etc.), collaboration with industry (through internship and co-supervision) and knowledge of societal issues (through collaboration with the Business School and the Scottish Graduate School for Social Sciences).

2) The academic community will benefit from a new generation of experts in Artificial Social Intelligence that will be in a position to advance AI driven interactive technologies (e.g., social robotics, artificial agents, Human-Machine Interaction, chatbots, conversational agents, etc.) through the informed use of Neuroscience and Psychology and, vice versa, advance Neuroscience and Psychology through the informed use of AI driven interactive technologies.

3) The industrial partners of the CDT will benefit from shaping the training of future experts in Artificial Social Intelligence. This will happen through co-supervision of PhD students with academics, hosting of students for internships, and designing of cohort training activities aimed at the development of professional skills.

4) The UK economy will benefit from the availability of a pool of highly skilled individuals (all connected through the network that will form during the cohort training years), trained not only from a scientific and technological point of view, but also in terms of skills aimed at facilitating the successful integration into professional environments (see point 1 above).

5) The lay audience will benefit from the public outreach activities aimed at informing people less familiar with Artificial Social Intelligence, including both its opportunities (new products, new jobs, etc.) and its contentious issues (privacy violation, ethical issues, etc.).


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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/S02266X/1 01/07/2019 31/12/2027
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2280395 Studentship EP/S02266X/1 01/10/2019 01/04/2024 Rhiannon Fyfe
2280511 Studentship EP/S02266X/1 01/10/2019 01/10/2023 Emily O'Hara O'Hara
2637191 Studentship EP/S02266X/1 01/10/2019 01/10/2023 Andrei Birladeanu Birladeanu
2446580 Studentship EP/S02266X/1 21/09/2020 21/09/2024 Maria Vlachou
2446613 Studentship EP/S02266X/1 21/09/2020 21/09/2024 Thomas Goodge
2447048 Studentship EP/S02266X/1 21/09/2020 21/09/2024 Robin Bretin
2445938 Studentship EP/S02266X/1 21/09/2020 21/09/2024 Jacqueline Borgstedt
2447153 Studentship EP/S02266X/1 21/09/2020 21/09/2024 Serena Dimitri
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2447273 Studentship EP/S02266X/1 21/09/2020 21/09/2024 Radu Razvan Chirila
2445632 Studentship EP/S02266X/1 21/09/2020 21/09/2024 Andreas Drakopoulos
2446644 Studentship EP/S02266X/1 21/09/2020 21/09/2024 Christopher Chandler
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2446337 Studentship EP/S02266X/1 01/10/2020 30/09/2024 Tobias Thejll-Madsen
2597841 Studentship EP/S02266X/1 13/09/2021 14/09/2025 Juliane Kloidt
2597838 Studentship EP/S02266X/1 13/09/2021 14/09/2025 Cristina Fiani
2598167 Studentship EP/S02266X/1 13/09/2021 14/09/2025 Aleksandra Wruk
2620751 Studentship EP/S02266X/1 13/09/2021 14/09/2025 Dominik Jan Szczepaniak
2618024 Studentship EP/S02266X/1 13/09/2021 14/09/2025 Nicole Lai
2598274 Studentship EP/S02266X/1 13/09/2021 14/09/2025 Dominik Jan Szczepaniak
2597939 Studentship EP/S02266X/1 13/09/2021 14/09/2025 Shaul Ashkenazi
2618023 Studentship EP/S02266X/1 13/09/2021 14/09/2025 Michal Pelikan
2598283 Studentship EP/S02266X/1 13/09/2021 14/09/2025 Nicole Lai
2597840 Studentship EP/S02266X/1 13/09/2021 14/09/2025 Rawan Zreik Srour
2598341 Studentship EP/S02266X/1 13/09/2021 14/09/2025 Michal Pelikan
2598170 Studentship EP/S02266X/1 13/09/2021 14/09/2025 Roberto Luciani
2597861 Studentship EP/S02266X/1 13/09/2021 14/09/2025 Elizabeth Jacobs