IMMUNE is a nine-month project funded by the AHRC (UKRI COVID-19 Research Call) in collaboration with Loughborough University.
The purpose of this research project is to apply a user-centred design approach to conduct research that contributes to our understanding of immunity and vaccination passport services as part of the UK's COVID-19 exit strategy.
These immunity or vaccination passports would allow individuals who have antibodies of the SARS-COV-2 to return back to work, travel or socialise without restrictions. Their use has formed part of many countries' exit plans. Yet, there is a dispute among scientists, policymakers and the public that such interventions are based on many uncertainties that could put public health at risk, infringe privacy, and lead to inequalities in society.
To better understand this phenomenon, we will engage with key stakeholders to address the following two questions:
- What are the possible unintended consequences and risks of immunity passports?
- What are the key stakeholders' requirements, resources, AI technologies and processes needed in the design of services around immunity or vaccination passports in order to mitigate any unintended consequences?
Our approach will involve interviews, surveys, focus groups and participatory design workshops. Key deliverables will be specifications for service design including blueprints, user journey maps and systems modelling.
Dr Isabel Sassoon - Dr Isabel Sassoon is a Senior Lecturer (Associate Professor) in Computer Science at Brunel University London, where she is also Director of Education in the Department of Computer Science. Her research examines reasoning in AI systems, including statistical modelling, automated reasoning, and large language models (LLMs), with a focus on how model outputs are justified, explained, and evaluated in real‑world contexts.
A unifying theme of her work is the use of computational argumentation as a formal framework for understanding and supporting reasoning in data‑driven systems. Her research contributions are in the fields of open science, explainable AI and decision support systems. She holds a PhD in Informatics from King’s College London, where her doctoral research developed a computational argumentation‑based approach to support statistical model selection, helping formalise how analysts justify modelling decisions given data, assumptions, and research objectives.
More recently, her work has focused on reasoning in large language models (LLMs). She investigates how computational argumentation can be used to evaluate, steer, and improve LLM reasoning, including how models respond to critical questioning, detect reasoning errors, and revise conclusions.
Her applied research spans healthcare and public policy, including serving as Brunel Lead Investigator on the UKRI AHRC‑funded IMMUNE project, which examined the risks and unintended consequences of COVID‑19 immunity certification systems. She has extensive experience teaching quantitative data analysis at postgraduate level. Prior to academia, she spent over a decade working in industry as a data science consultant, including at SAS UK.
Isabel is a fellow of the Royal Statistical Society and Editorial Board Member of Real World Data Science,