By Brent Daniel Mittelstadt (Editor) and Luciano Floridi (Editor)
This book presents cutting edge research on the new ethical challenges posed by biomedical Big Data technologies and practices.

Dr. Brent Mittelstadt is the OII’s Director of Research and a Senior Research Fellow. He also coordinates of the Governance of Emerging Technologies (GET) research programme which works across ethics, law, and emerging information technologies. He is a leading data ethicist and philosopher specializing in AI ethics, professional ethics, and technology law and policy.
In his current role he leads the Trustworthiness Auditing for AI project, a three-year multi-disciplinary project with University of Reading cutting across ethics, law, computer science, and psychology to determine how to use AI accountability tools most effectively to create and maintain trustworthy AI systems. He also co-leads the A Right to Reasonable Inferences in Advertising and Financial Services project which examines opportunities and challenges facing sectoral implementation of a right to reasonable inferences in advertising and financial services.
Dr. Mittelstadt is the author of highly cited works across topics including the ethics of algorithms, artificial intelligence (AI), and Big Data; fairness, accountability, and transparency in machine learning (ML); data protection and non-discrimination law; group privacy; ethical auditing of automated systems; digital epidemiology and public health ethics; and ethical design of personal health monitoring technologies.
Across these areas he has contributed several key policy analyses, technical fixes, and ethical frameworks to address the most pressing risks of emerging data-intensive technologies. These include (1) legal analysis of the enforceability of a “right to explanation” of automated decisions in the General Data Protection Regulation (GDPR); (2) the development of a method and ethical requirements for providing “meaningful explanations” of automated decisions in the form of ‘counterfactual explanations’; (3) a novel, legally compliant fairness metric to detect bias in AI and machine learning systems (‘Conditional Demographic Disparity’); and (4) a classification scheme for fairness metrics based on non-discrimination law. These contributions are widely cited and have been implemented by researchers, policy-makers, and industrial bodies internationally, featuring in policy proposals and guidelines from the UK government, Information Commissioner’s Office, and European Commission, as well as products from Google, Amazon, and Microsoft.
Dr. Mittelstadt is the recipient of several prestigious awards recognising the impact of his work on scholarship, policy, and society. In 2018 and 2021 he received O2RB Excellence in Impact Awards for his work on explanations in AI, counterfactual explanations, and AI fairness and bias in non-discrimination law. In 2019 he was delighted to receive the best paper award at the Privacy Law Scholars Conference (PLSC) for his work on the ‘right to reasonable inferences’ in data protection law.
Dr. Mittelstadt has substantial experience in leading and coordinating multi-disciplinary work across large-scale, multi-partner projects. His work has been funded by a variety of fellowships including a British Academy Postdoctoral Fellowship as well as research grants from funders such as the Wellcome Trust, Department of Health and Social Care, Sloan Foundation, Miami Foundation, and Luminate Group. In addition to his current roles, Dr. Mittelstadt previously led a team of researchers on the ROADMAP project working on the ethics and governance of a pan-European data platform for Alzheimer’s research. He also led work on the ‘Privacy and Trust’ stream as part of the EPSRC PETRAS National Centre of Excellence for loT Systems Cybersecurity.
Ethics; data ethics; moral philosophy; applied ethics; AI ethics; professional ethics; medical ethics; technology governance and policy; data protection law; non-discrimination law; fairness, accountability, and transparency in machine learning; medical expert systems; epistemology
By Brent Daniel Mittelstadt (Editor) and Luciano Floridi (Editor)
This book presents cutting edge research on the new ethical challenges posed by biomedical Big Data technologies and practices.
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.
This project transforms the concept of counterfactual explanations into a practically useful tool for explaining automated black-box decisions.
In the past five years my work has been financially supported by the Wellcome Trust, Sloan Foundation, Department of Health and Social Care via the AI Lab at NHSx, British Academy, John Fell Fund, Innovative Medicines Initiative, Alan Turing Institute, Engineering and Physical Sciences Research Council, Miami Foundation, and Luminate Group. As part of my science communication and policy outreach, I have served in an advisory capacity for the Office for National Statistics, Fable Data, Mind Foundry, and GSK Consumer Healthcare. I have also received reimbursement for conference-related travel from funding provided by DeepMind Technologies Limited.
I conduct my research in line with the University's academic integrity code of practice.
With Dr Brent Mittelstadt
Workshop bringing together expertise to address emerging challenges in the field, and the requirements for a European framework for ethical usage of biomedical Big Data.
With Professor Luciano Floridi, and Dr Brent Mittelstadt
Workshop bringing together expertise to address emerging challenges in the field, and the requirements for a European framework for ethical usage of biomedical Big Data.
14 July 2021
Leading experts in ethics and law Dr Silvia Milano, Dr Brent Mittelstadt and Prof Sandra Wachter, Oxford Internet Institute look beyond the algorithm to consider the impact of online targeted advertising on consumers and the potential online harms.
27 April 2021
Leading experts in ethics and law from the Oxford Internet Institute believe current European Union (EU) legislation is failing to ensure consumers do not miss out on products and offers based on their online behaviour.
21 April 2021
A new method to detect discrimination in AI and machine learning systems created by academics at Oxford Internet Institute, has been implemented by Amazon in their bias toolkit, ‘Amazon SageMaker Clarify’, for use by Amazon Web Services customers.
8 March 2021
Leading experts in ethics and law from the Oxford Internet Institute (OII) believe that the ways AI systems are commonly designed to measure bias and fairness clash with the fundamental aims of EU non-discrimination law.
This course will introduce to some of the fundamental questions that have been raised in this domain across the social sciences.