Ethical AI Coding with Synthetic Administrative Data

This course explores the ethical and responsible use of local AI models to support the development of code for analysing synthetic administrative data. Participants will examine key considerations around data privacy, transparency, reproducibility, and the appropriate use of generative AI in research workflows. Through practical, hands-on exercises, the course will demonstrate how local AI tools can be used to assist with data preparation, coding, documentation, and analytical problem-solving while maintaining high standards of research integrity. Drawing on real-world examples and best practice guidance, participants will gain the skills and confidence to incorporate AI-assisted coding into their analytical work safely, effectively, and responsibly.

• Introduction to AI for coding.
• Using local AI models for coding.
• Introduction to agentic harnesses for coding.
• The ethics of using AI for data analysis, particularly synthetic administrative data.
• Practice sessions

Course location

In person is strongly recommended and will be held at UK Research & Innovation’s London Office, 157 to 197 Buckingham Palace Road, London, SW1W 9SP, room 5.02 A&B (this is a Government building so please bring an official picture ID for admission)

Online will be held on Teams – the joining link will be provided after enrolment.

When registering, you will be asked whether or not you plan to attend the course in person or online.

Who should attend?

This course is suitable for researchers interested in understanding how to ethically use local AI models for administrative data analysis.

Course pre-requisite

Some familiarity with Python / R and data analysis is recommended but not required. Also, ensure that Python / R environments are setup, LM Studio and pi.dev are also installed. Participants need to be registered with UKDS and download a synthetic dataset from their website.

Skills areas & competencies

Skills area(s)

• Problem Solving, Analysis, Statistical Modelling, Visualisation

Skills competencies

• Data Analysis and Model Building

Learning outcomes

At the end of the course, attendees should be able to:

• Evaluate and assess the ethical implications of using local AI models for synthetic administrative data analysis.
• Evaluate, identify, setup and use appropriate local AI models for data analysis.
• Able to install and run agentic harnesses for AI-enabled data analysis.

Transferrable skills

• AI-enabled data analysis and coding.

Course schedule

Tutor profiles

Dr Kamran Soomro

I am an interdisciplinary researcher and Associate Professor of Artificial Intelligence in the School of Computing and Creative Technologies at UWE Bristol. My work sits at the intersection of computing, urban planning, and civic governance, drawing on methods from data planning, generative AI, and digital twins to address complex societal challenges. I hold a Bachelor of Engineering in Computer Software Engineering from the National University of Sciences and Technology and a PhD in Computer Science from the University of the West of England. Over the past decade, I have focused on bridging Data Science and Urban Informatics, with a growing emphasis on participatory approaches that bring communities, data, and AI into shared decision-making.

You can view my personal website here.

Dr Kat Fair

Dr Kat Fair is a Lecturer in Data Science whose work focuses on labour mobility, skills evolution, and the dynamics of job transitions. She develops empirically grounded agent-based models and complementary data-science methods to study how workers move through labour markets and how skills accumulate, erode, and mismatch over time. Her research draws on large-scale administrative and survey data and is used to inform evidence-based policy analysis with UK government partners.

At UWE, she teaches across data-science curricula, emphasising reproducible analysis, and the critical interpretation of complex datasets. She is active in policy engagement, contributing analytical insights on skills, labour-market resilience, and the implications of emerging technologies for work.

Visit my website