Emerging technologies such as artificial intelligence (AI), machine learning, and advanced data analytics both shape and are shaped by society, posing novel challenges that must be addressed across legal, ethical, and social dimensions.
As these technologies evolve, there is a need for robust, interdisciplinary research to ensure that they are governed in a cohesive way that maximizes societal benefits while minimizing harm.
Legal, ethical, and technological experts must come together to develop solutions that are not only legally sound but also morally responsible and technically feasible.
Professor of Data Ethics and Policy, Director of Research
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.
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
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.
Within the Governance of Emerging Technologies (GET) research group, we investigate the governance of new technologies through an inclusive, interdisciplinary lens. Our work spans law, philosophy, computer science, and related disciplines, and focuses on how to design, deploy and govern emerging technologies.
Our research projects address critical issues such as:
These challenges demand expertise not just in law and ethics but also in the technical aspects of AI and machine learning. Our interdisciplinary approach ensures that we consider these issues from multiple perspectives, including legal requirements, ethical considerations and technical feasibility.
Through interdisciplinary research and collaboration, we are working to ensure that powerful emerging technologies are developed and deployed for the public good.
Our work has been recognised through awards such as Cognition X, the Computer Weekly Awards, the Privacy Law Scholars Conference (PLSC), and the O2RB Excellence in Impact Awards. Our research on AI explainability has been cited in influential reports on data governance by the UK Government and European Commission. The counterfactual explanation method we developed has been implemented by leading tech companies like Google, Vodafone, IBM, and Accenture. Our proposal for a ‘right to reasonable inferences’ to protect privacy in the era of big data was cited in discussions on the GDPR and featured in the UK’s 2021 Research Excellence Framework (REF). Additionally, our fairness measure, ‘conditional demographic disparity,’ has shaped the EU Artificial Intelligence Act and been adopted by Amazon in their cloud computing and machine learning products.
Through our research, we continue to inform policy, influence international discussions on technology ethics, and provide practical solutions for designing AI systems that are fair, accountable, and transparent.
This programme supports research on AI, Government and Policy.
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.
This OII research programme investigates legal, ethical, and social aspects of AI, machine learning, and other emerging information technologies.
Former Postdoctoral Researcher
Daria Onitiu was a Postdoctoral Researcher within the Trustworthiness for AI Auditing project. She is interested in the facets and boundaries of the law to address pressing societal problems caused by novel technologies.
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.
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 Postdoctoral Researcher
Zihao Fu was in the Trustworthiness Auditing for AI project. His research interests included Natural Language Processing, Large Language Model, Machine Learning, Biomedical Applications, Large Language Model safety fairness among other areas.
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.
