The Governance of Emerging Technologies (GET) research programme at the Oxford Internet Institute investigates legal, ethical, and social aspects of AI, machine learning, and other emerging information technologies.
New technologies shape, and are shaped by, society. In choosing how to govern emerging technologies, we must encourage beneficial developments while not losing sight of the essential rights and values upon which democratic societies are built. International debate on the legal and ethical governance of AI and other emerging technologies increasingly recognises the need for an interdisciplinary approach, most commonly thought to require expertise in law, ethics, and computer science or machine learning at a minimum.
Within GET we investigate how to design, deploy, and govern the new technologies that pose novel challenges across law, philosophy, computer science, and related disciplines. Our research projects include issues such as data protection and inferential analytics, algorithmic bias, fairness, diversity and non-discrimination as well as explainable and accountable AI. These are areas where interdisciplinary thinking is pivotal.
The unique challenges of emerging technologies demand a holistic and multi-disciplinary approach to governance to investigate what:
Reflecting these aims, our work to date has broadly focused on three themes:
Our research portfolio will continue to expand in the future, and we’re always looking for new collaborators interested in these and related topics concerning the legal and ethical governance of emerging technologies.
The programme is coordinated by Prof. Sandra Wachter, a legal scholar; Prof. Brent Mittelstadt, an ethicist; and Prof. Chris Russell – a specialist in machine learning.
The programme has had a significant impact in academia, policy, and civil society. This has been recognised through a variety of award schemes including Cognition X, the Computer Weekly Awards, the Privacy Law Scholars Conference (PLSC), and the O2RB Excellence in Impact Awards. Our work on AI explainability has been cited in numerous influential reports on data governance (including by the UK Government and European Commission). The method we developed to compute understandable explanations of AI decisions, or ‘counterfactual explanations’, has been implemented in products from leading technology companies including Google, Vodafone, IBM and Accenture. Our innovative proposal for a ‘right to reasonable inferences’, which is designed to protect privacy in the age of big data, has been cited in commentaries on the General Data Protection Regulation (GDPR) and was highlighted in the UK’s 2021 Research Excellence Framework (REF). Our proposed fairness measure to combat AI bias and discrimination, called ‘conditional demographic disparity’, has influenced the EU Artificial Intelligence Act and has been implemented by Amazon into their cloud computing and machine learning products.
Professor of Technology and Regulation
Professor Sandra Wachter is Professor of Technology and Regulation focusing on law and ethics of AI, Big Data, and robotics as well as Internet regulation at the Oxford Internet Institute at the University of Oxford
Professor of Data Ethics and Policy
Professor Brent Mittelstadt is a data ethicist and philosopher specializing in AI ethics, professional ethics, and technology law and policy. He is the founder/coordinator of the Governance of Emerging Technologies (GET) programme.
Dieter Schwarz Associate Professor, AI, Government & Policy.
Chris Russell is the Dieter Schwarz Associate Professor, AI, Government and Policy. Dr Russell’s work lies at the intersection of computer vision and responsible AI.
British Academy Postdoctoral Fellow
Johann Laux works at the intersection of law and the social sciences. His current research is interested in the governance of emerging technologies as well as the design of institutions.
Research Associate
Netta Weinstein is a professor of clinical and social psychology at the University of Reading. She studies human motivation, behaviour and well-being in collaboration with Professors Wachter and Mittelstadt.
Research Associate
Dr Daria Onitiu researches and publishes on the legal, ethical and governance aspects surrounding Artificial Intelligence (AI) technologies, generative AI and deepfakes.
PhD Student, University of Reading
Lizzie is Associate Medical Director at Annalise.AI, a former NHS doctor and also a PhD student at the University of Reading. She has a particular interest in the clinical use of medical imaging AI.
Placement Student, University of Reading
Fyonna Boateng is a Psychology undergraduate at the University of Reading. With Dr. Netta Weinstein, she is investigating public opinions of AI and its potential biases.
Academic Visitor
Eoin was a postdoctoral researcher on the Trustworthiness Auditing in AI project. His research interests include explainable and trustworthy AI, interpretable machine learning and evaluating robustness, stability and uncertainty in explanation.
Former Recognised Student
Sofie is a Recognized Student at the OII. Her research is focused on ethical machine learning.
DPhil Student
Will has an interest in algorithmic fairness, explainability, and wider AI ethics. He currently works in DeepMind's Ethics team, and was previously a consultant within PwC's Sustainability & Climate Change team.
DPhil Student
Jazmia Henry is a part-time DPhil candidate for the Social Data Science programme at the University of Oxford. Her research interests include Explainable AI, Large Language Models, AI Alignment and AI Ethics.
Postdoctoral Researcher, University of Reading
Anna is a Postdoctoral Researcher working to examine factors that shape trustworthiness in healthcare technology and decision-making regarding transparent communication about potential bias in AI.
Former Postdoctoral Researcher
Prashan Madumal is a computer scientist specialising in Artificial Intelligence. His work mainly focuses on Explainable and Trustworthy AI, Reinforcement learning and Causal Learning.
Former Research Associate
Silvia is a philosopher interested mainly in epistemology and ethics of Artificial Intelligence, working with Dr Brent Mittelstadt.
Former Recognised Student
Paula Pedigoni Ponce is a Recognised Student at the OII. Her research is focused on the regulation of health personal data in Brazil.
Former MSc Student
Trisha was an OII Rhodes Scholar interested in building tech and policy to counter harmful online speech. She is the inventor of ReThink, an anti-hate app, and holds a BA in Gov & Tech Science from Harvard.
Former Postdoctoral Researcher
Kaivalya worked on AI explainability and fairness at Oxford. He built upon his work auditing and testing machine learning models in industry and in academia, making AI systems safe and trustworthy.
Former Visiting Fellow
Yashwinie Shivanand is a Visiting Research Fellow in ethical artificial intelligence, specializing in trustworthy AI policy and practice.
Former Recognised Student
Jake is a PhD candidate in philosophy with the Australian National University and a Recognised Student at the Oxford Internet Institute.
7 July 2025
Whether it is merely long-term hype or near-term reality, Large Language Models (LLMs) in medicine are well-positioned to engage with a wide range of medical tasks and be used for medical research purposes.
7 May 2025
New Oxford study uncovers explosion of accessible deepfake AI image generation models intended for the creation of non-consensual, sexualised images of women.
19 August 2024
Professor Sandra Wachter highlights the regulatory loopholes in EU AI legislation and proposes how these can be closed to address key ethical issues around the development of AI.
7 August 2024
Leading experts in regulation and ethics at the Oxford Internet Institute, part of the University of Oxford, have identified a new type of harm created by LLMs which they believe poses long-term risks to democratic societies and needs to be addressed
The Independent, 23 June 2025
Most deepfake pictures need only one picture to create a semi-believable facial model and experts say deepfakes can make voices indistinguishable from the real thing with just a few minutes of audio. Prof Sandra Wachter comments.
The Guardian, 15 June 2025
Healthy scepticism is needed because flaw is that large language models remain prone to casually making things up
New Scientist, 30 May 2025
Artificial intelligence has removed many of the barriers to understanding a new language, but there are still good reasons to do things the old-fashioned way
Somewhere on Earth: The Global Tech Podcast, 20 May 2025
A new study reveals nearly 35,000 publicly downloadable AI models capable of generating deepfake pornography—often targeting women and celebrities.
This project will develop useful and responsible machine learning methods to achieve real-world early detection and personalised disease outcome prediction of inflammatory arthritis.
This project will evaluate the effectiveness of accountability tools addressing explainability, bias, and fairness in AI. A ‘trustworthiness auditing meta-toolkit’ will be developed and validated via case studies in healthcare and open science.
The project will identify weaknesses in general and sectoral regulatory mechanisms—such as the limited protections afforded to inferences in data protection law—and argue for greater accountability by establishing a ‘right to reasonable inferences'.
