How data science and statistics can shape the UK’s AI strategy

About the panelists

Andrew Garrett (chair) is president of the Royal Statistical Society. He is executive vice president of scientific operations at the clinical research organisation ICON plc, where he is responsible for the strategic direction and operational delivery of a range of clinical trial services. Having worked extensively in the area of rare diseases, he has held various biostatistics managerial positions in the pharmaceutical industry, including vice president of biostatistics, medical writing and regulatory affairs at Quintiles (now IQVIA).

Peter Wells is a technologist, who accidentally started a second career in public policy. He has both worked on AI policy and helped design AI-enabled services. After 20 years in the telecoms industry, he found himself spending 2014 developing digital government policy for the Labour Party. Since then he has worked with multiple governments and organisations including the Open Data Institute, Projects by IF, Google, Meta and the Government Digital Service.

Maxine Setiawan is a data scientist specialising in AI and data risk and trusted AI in EY UK&I. She works to help clients from various industries assess and manage risks from analytics and AI systems, and implement AI governance to ensure AI systems are implemented with fair, accountable, and trustworthy principles. She combines her socio-technical background with an MSc in Social Data Science from the University of Oxford, and her experience working in data science within consulting firms.

Sophie Carr is chair of the Real World Data Science editorial board and is the founder and owner of Bays Consulting, a data science company. Having trained as an aeronautical engineer, Sophie completed her PhD in Bayesian analysis part time whilst she worked and, following redundancy, founded her own company. She is the VP for education and statistical literacy at the RSS and sits on the executive committees of the Academy for Mathematical Sciences and the International Centre for Mathematical Sciences. She is also currently the world’s most interesting mathematician.

Chris Nemeth is a professor of statistics at Lancaster University. His primary research area is in probabilistic machine learning and computational statistics. He holds an EPSRC-funded Turing AI fellowship on Probabilistic Algorithms for Scalable and Computable Approaches to Learning (PASCAL), and through his fellowship he works closely with partners including Shell, Tesco, Elsevier, Microsoft Research and The Alan Turing Institute. He is chair of the Royal Statistical Society Section on Computational Statistics and Machine Learning.

Karen Tingay is a principal statistical methodologist at the Office for National Statistics where she specialises in natural language processing and in managing complex survey imputation. She established and heads up the Text Data Subcommunity, a large network of public sector analysts to build capability and best practice guidance in managing and analysing unstructured text data, on behalf of the Government Data Science Community. She sits on several cross-government and international working groups on responsible use of generative AI.

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This article is licensed under a Creative Commons Attribution 4.0 (CC BY 4.0) International licence.

How to cite: Tarran, Brian. 2023. “How data science and statistics can shape the UK’s AI strategy.” Real World Data Science, October 30, 2023. URL