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Portrait
Prof. Brent Mittelstadt

Prof. Brent Mittelstadt

Principal Investigator
About Contact Fields of Research Selected Publications

About

Brent Mittelstadt is Principal Investigator of the research group “Ethics and Governance of Innovation” at the Weizenbaum Institute, and Professor of Data Ethics and Policy at the Oxford Internet Institute, University of Oxford where he leads the Governance of Emerging Technologies (GET) research programme.

He is a data ethicist working across philosophy, law, and emerging information technologies with a focus on AI ethics, algorithmic fairness and explainability, technology law and policy, and innovation ideologies. He is the author of highly cited foundational works addressing the ethics of algorithms, AI, and Big Data; truth and accuracy in large language models (LLMs); fairness, accountability, and transparency in machine learning; data protection and non-discrimination law; group privacy; ethical auditing of automated systems; and digital epidemiology and public health ethics. Brent currently serves on the IAPP AI Governance Center’s Advisory Board and the Editorial Boards of Big Data & Society and the Journal of AI Law and Regulation. He has previously served as advisor to the Council of Europe, Responsible AI Institute, British Standards Institute, Equinet, Centre for Data Ethics and Innovation, the UK Medicines and Healthcare products Regulatory Agency (MHRA) and the Department for Culture, Media and Sport.

Contact

E-Mail
brent.mittelstadt@oii.ox.ac.uk
Organisation
Oxford Internet Institute (OII)

Fields of Research

  • Data ethics
  • Technology law
  • AI ethics
  • Philosophy

Selected Publications

Selection

Stone, J. & Mittelstadt, B. 2025. Legitimate Power, Illegitimate Automation: The problem of ignoring legitimacy in automated decision systems. ACM Journal on Responsible Computing.

Onitiu, D., Wachter, S. & Mittelstadt, B. 2025. Walking backwards to ensure Risk Management of Large Language Models in Medicine. Journal of Law, Medicine & Ethics.

Hawkins, W., Mittelstadt, B. and Russell, C., 2025. Deepfakes on Demand: The rise of accessible non-consensual deepfake image generators. In Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency (pp. 1602-1614).

Macfarlane Smith, G., Luka, N., Lattimore, B.R., Newman, J., Nonnecke, B. and Mittelstadt, B., 2025. Responsible Generative AI Use by Product Managers: Recoupling Ethical Principles and Practices. In Academy of Management Proceedings (Vol. 2025, No. 1, p. 24377). Valhalla, NY 10595: Academy of Management.

Hacker, P., Mittelstadt, B., Hammer, S. and Engel, A. (eds.), 2025. Oxford Handbook on the Foundations and Regulation of Generative AI. Oxford University Press.

Hacker, P., Engel, A., Hammer, S., and Mittelstadt, B. 2025 (in press). Introduction to the Foundation and Regulation of Generative AI. In: Hacker, P., Hammer, S., Engel, A. and Mittelstadt, B. (eds). 2025. Oxford Handbook on Generative AI: Technical, Social and Legal Challenges. Oxford University Press.

Hacker, P., Borgesius, F. Z., Mittelstadt, B. & Wachter, S. 2025 (in press). Generative Discrimination: What Happens when Generative AI Exhibits Bias, and What Can be Done About It. In: Hacker, P., Hammer, S., Engel, A. and Mittelstadt, B. (eds). 2025. Oxford Handbook on Generative AI: Technical, Social and Legal Challenges. Oxford University Press.

Tovmasyan, A., Weinstein, N., and Mittelstadt, B. 2025. Values tensions and values tradeoffs in the development of healthcare artificial intelligence technology: a conceptual model of decisions to create trustworthy technology. Social Influence 20.1 (2025): 2478940.

Tovmasyan, A., Onitiu, D., Wachter, S., Mittelstadt, B. and Weinstein, N., 2025. Autonomy over authority: the role of autonomous motivation in law compliance. Motivation and Emotion.

Tovmasyan, A., Liefgreen, A., Wachter, S., Mittelstadt, B. and Weinstein, N., 2025. Motivating transparent communications about bias in healthcare technology development. Collabra: Psychology, 11(1), p.136456.

Mittelstadt, B. 2025. How to use the Artificial Intelligence Act to investigate AI bias and discrimination: A guide for EU Equality Bodies. Report prepared on behalf of Equinet, the European Network of Equality Bodies.

Wachter, S., Mittelstadt, B. & Russell, C. 2024. Do large language models have a legal duty to tell the truth? Royal Society Open Science, 11(8).

Onitiu, D., Wachter, S. & Mittelstadt, B. 2024. How AI challenges the Medical Device Regulation: Patient safety, benefits, and intended uses. Journal of Law and the Biosciences, lsae007. [IF (2022): 6.066].

Laux, J., Wachter, S. & Mittelstadt, B. 2024. Three Pathways for Standardisation and Ethical Disclosure by Default under the European Union Artificial Intelligence Act. Computer Law & Security Review, 53, 105957. [IF (2023): 3.3].

Mittelstadt, B., Wachter, S. & Russell, C. 2024. The Unfairness of Fair Machine Learning: Levelling Down and Strict Egalitarianism by Default. Michigan Technology Law Review 2024 30:1.

Delaney, E., Fu, Z., Wachter, S., Mittelstadt, B. & Russell, C. 2024. OxonFair: A Flexible Toolkit for Algorithmic Fairness. Proceedings of NeurIPS 2024 – Main Track.

Hawkins, W., Mittelstadt, B. and Russell, C. 2024. The effect of fine-tuning on language model toxicity. Proceedings of NeurIPS 2024 Workshop SafeGenAI.

Gillis, R., Laux, J. & Mittelstadt, B. 2024. Trust and Trustworthiness in AI.  In: Paul, R., Carmel, E. and Cobbe, J. (eds). Handbook on Public Policy and Artificial Intelligence, Edward Elgar.

Mittelstadt, B., Wachter, S. & Russell, C. 2023. To protect science, we must use LLMs as zero-shot translators. Nature Human Behaviour, 7, 1830–1832. [IF (2022): 29.9].

Liefgreen, A., Weinstein, N., Wachter, S. & Mittelstadt, B. 2023. Beyond ideals: why the (medical) AI industry needs to motivate behavioural change in line with fairness and transparency values, and how it can do it. AI & Society [IF (2021): 2.868]

Laux, J., Wachter, S. & Mittelstadt, B. 2023. Trustworthy Artificial Intelligence and the European Union AI Act: On the Conflation of Trustworthiness and the Acceptability of Risk. AI & Society. [IF (2021): 2.868]

Hawkins, W. & Mittelstadt, B. 2023. The ethical ambiguity of AI data enrichment: Measuring gaps in research ethics norms and practices. Proceedings of FAccT 2023 - ACM Conference on Fairness, Accountability, and Transparency, 261-270.

Laux, J., Stephany, F., Russell, C., Wachter, S. & Mittelstadt, B. 2022. The Concentration-after-Personalisation Index (CAPI): Governing Effects of Personalisation Using the Example of Targeted Online Advertising. Big Data & Society 9(2). [IF (2022): 8.731].

Mittelstadt, B. 2022. Protecting health privacy through reasonable inferences. The American Journal of Bioethics, 22(7), 65-68. [IF (2020): 11.23).

Mittelstadt, B. 2022. Interpretability and Transparency in Artificial Intelligence. In: Veliz, C. (ed). Oxford Handbook of Digital Ethics, Oxford University Press.

Mittelstadt, B. 2022. Impact of AI on the doctor-patient relationship. Council of Europe.

Mittelstadt, B. 2021. Near-term ethical challenges of digital twins. Journal of Medical Ethics, 47(6), 405-406. [IF (2020): 2.916].

Wachter, S., Mittelstadt, B. & Russell, C. 2021. Why fairness cannot be automated: Bridging the gap between EU non-discrimination law and AI. Computer Law & Security Review 41, 105567. [IF (2021): 3.30; Awarded O2RB Excellence in Impact Award in 2021].

Laux, J., Wachter, S. & Mittelstadt, B. 2021. Taming the few: Platform regulation, independent audits, and the risks of capture created by the DMA and DSA. Computer Law & Security Review 43, 105613. [IF (2021): 3.30].

Wachter, S., Mittelstadt, B. & Russell, C. 2021. Bias preservation in machine learning: the legality of fairness metrics under EU non-discrimination law. West Virginia Law Review 123, 735. [Awarded O2RB Excellence in Impact Award in 2021].

Milano, S., Mittelstadt, B., Wachter, S. & Russell, C. 2021. Epistemic fragmentation poses a threat to the governance of online targeting. Nature Machine Intelligence, 3. [IF (2020): 15.508].

Laux, J., Wachter, S. & Mittelstadt, B. 2021. Neutralizing online behavioural advertising: algorithmic targeting with market power as an unfair commercial practice. Common Market Law Review 58. [IF (2020): 3.257].

Woodcock, C., Blank, G. & Mittelstadt, B. 2021. The Impact of Explanations on Layperson Trust in AI-Driven Symptom Checker Applications: An Experimental Study. Journal of Medical Internet Research, 23(11), e29386. [IF (2020): 5.428].

Mittelstadt, B. & Kwakkel, J. 2021. Assessing Provenance and Bias in Big Data. In: Doorn, N. and Michelfelder, D. (eds.). The Routledge Handbook of Philosophy of Engineering, Routledge.

McKeown, A., Keenan, A. […] & Mittelstadt, B. 2020. Health Outcome Prioritisation in Alzheimer’s Disease: Understanding the Ethical Landscape. Journal of Alzheimer’s Disease, 77(1). [IF (2020): 3.909].

Angehrn, Z., […] & Mittelstadt, B., and de Reydet-de-Vulpillieres, F. 2020. Ethical and Social Implications of Using Predictive Modelling for Alzheimer’s Disease Prevention: A Systematic Literature Review. Journal of Alzheimer’s Disease, 1-18. [IF (2020): 3.909].

Mittelstadt, B. 2019. Principles Alone Cannot Guarantee Ethical AI. Nature Machine Intelligence, 2019(1), 501-507. [IF (2020): 15.508].

Wachter, S. & Mittelstadt, B. 2019. A Right to Reasonable Inferences: Re-thinking Data Protection Law in the Age of Big Data and AI. Columbia Business Law Review, 2019(2), 494-620, doi.org/10.7916/cblr.v2019i2.3424. [Awarded Privacy Law Scholars Conference (PLSC) Junior Scholars Award in 2019].

Mittelstadt, B., Russell, C. & Wachter, S. 2019. Explaining Explanations in AI. Proceedings of ACM FAT* ’19: Conference on Fairness, Accountability, and Transparency, Atlanta, GA, USA. ACM, New York, NY, USA.

Gallacher, J., de Reydet de Vulpillieres, F., Mittelstadt, B. et al. 2019. Challenges for Optimizing Real-World Evidence in Alzheimer’s Disease: The ROADMAP Project. Journal of Alzheimer’s Disease, 67(2), 495-501. [IF (2020): 3.909]

Mittelstadt, B., Benzler, et al. 2018. Is There a Duty to Participate in Digital Epidemiology? Life Sciences, Society & Policy, 14(9). [IF (2020): 1.450].

Wachter S., Mittelstadt, B. & Russell, C. 2018. Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR. Harvard Journal of Law & Technology, 31(2), 841-887. [Awarded O2RB Excellence in Impact Award in 2018].

Cath, C., Wachter, S., Mittelstadt, B., Taddeo, M. & Floridi, L. 2018. Artificial Intelligence and the 'Good Society': The US, EU, and UK approach. Science and Engineering Ethics, 24, 505-528. [IF (2020): 3.525].

Mittelstadt, B. 2018. From Individual to Group Privacy in Biomedical Big Data. In: Cohen, I.G., Lynch, H.F., Vayena, E., Gasser, U. (eds.). Big Data, Health Law, and Bioethics, Cambridge University Press, Cambridge, 172-192, doi.org/10.1017/9781108147972.017.

Wachter, S., Mittelstadt, B. & Floridi, L. 2017. Why a Right to Explanation of Automated Decision-making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2), 76-99. [IF (2019): 4.162].

Mittelstadt, B. 2017. From Individual to Group Privacy in Big Data Analytics. Philosophy & Technology, 30, 475-494. [IF (2020): 4.760].

Mittelstadt, B. 2017. Ethics of the Health-Related Internet of Things: A Narrative Review. Ethics and Information Technology, 19(3), 157-175. [IF (2020): 4.449].

Mittelstadt, B. 2017. Designing the Health-Related Internet of Things: Ethical Principles and Guidelines. Information, 8(3), 77. [IF (2020): 0.52].

Mittelstadt, B. 2016. Auditing for Transparency in Content Personalisation Systems. International Journal of Communication, 10(0), 12. [IF (2020): 1.802].

Mittelstadt, B., Allo, P., Taddeo, M., Wachter S. & Floridi, L. 2016. The Ethics of Algorithms: Mapping the Debate. Big Data & Society, 3(2), 1-21. [IF (2022): 8.731].

Mittelstadt, B. and Floridi, L. (eds.) 2016. The Ethics of Biomedical Big Data. 1st ed. Law, Governance and Technology, Springer International Publishing.

Stahl B.C., Timmermans, J. & Mittelstadt, B. 2016. The Ethics of Computing: A Survey of the Computing-Oriented Literature. ACM Computing Surveys, 48(4). [IF (2020): 10.282].

Mittelstadt, B. & Floridi, L. 2016. The Ethics of Big Data: Current and Foreseeable Issues in Biomedical Contexts. Science and Engineering Ethics, 22(2). [IF (2020): 3.525].

Mittelstadt B., Stahl B.C. & Fairweather N.B. 2015. How to Shape a Better Future? Epistemic Difficulties for Ethical Assessment and Anticipatory Governance of Emerging Technologies. Ethical Theory and Moral Practice, 18, 1027–1047. [IF (2020): 1.780].