We can currently only accept payments by Visa, Mastercard and PayPal. We cannot accept discount codes. Apologies for any inconvenience.
Dr Miri Zilka
PhD in Analytical Science (Physics), MSc in Physics, Dual BSc in Physics and Biology
Assistant Research Professor and Leverhulme Research Fellow, Machine Learning Group
College Research Associate at King’s College
Associate Fellow at the Leverhulme CFI
About me
I am Assistant Professor in Responsible Machine Learning at the University of Cambridge, and my work focuses on how AI is used in decisions that change people’s lives. Much of my research looks at high-stakes settings such as criminal justice and social care, where individuals cannot opt out and mistakes can cause real harm.
I am interested in how we can build AI that genuinely enables humans, not the other way around, and how organisations can move beyond the hype to adopt AI responsibly.
Before starting my current role, I was a Leverhulme Early Career Fellow in Machine Learning. Before joining the University of Cambridge, I was a Research Fellow in Machine Learning at the University of Sussex. I hold a PhD in Analytical Science (Physics) from the University of Warwick.
Through this course I want to empower leaders and decision makers to develop an AI strategy that truly delivers value to their organisation. There is no one-size-fits-all strategy. The goal of this course is to guide the process of asking right questions about AI and making decisions one can stand behind – grounded in your own organisational context and needs rather than the shiniest new technology.
Courses
Awards
2021: Leverhulme ERC Fellowship, University of Cambridge
Roles
Assistant Professor in Responsible Machine Learning, Department of Engineering and the Leverhulme Centre of the Future of Intelligence, University of Cambridge.
Publications
Zilka, M. et al. (2026). Negotiating risk boundaries in AI for policing through mixed-stakeholder deliberation. AAI/ACM Conference on AI, Ethics, and Society (AIES), forthcoming 12 October 2026.
Zilka, M. et al (2026). Human–AI interaction for time-critical sensemaking in missing persons investigations(Opens in a new window). Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems, 395, 1–15.
Zilka, M. et al (2025). From incidents to insights: Patterns of responsibility following AI harms(Opens in a new window). Equality and Access in Algorithms, Mechanisms, and Optimizations (EAAMO), 151–69.
Zilka, M. et al (2024). Beyond use-cases: A participatory approach to envisioning data science in law enforcement(Opens in a new window). ACM Conference on Fairness, Accountability, and Transparency (FAccT), 1809–26.
Zilka, M. et al (2024). Optimising human–machine collaboration for efficient high-precision information extraction from text documents(Opens in a new window). ACM Journal on Responsible Computing, 1(2), 16, 1–27.
Zilka, M. et al (2024). Evaluating language models for mathematics through interactions(Opens in a new window). In Proceedings of the National Academy of Sciences, 121(24), e2318124121.
Zilka, M. et al (2023). The progression of disparities within the criminal justice system: Differential enforcement and risk assessment instruments(Opens in a new window). ACM Conference on Fairness, Accountability, and Transparency (FAccT), 1553–69.
Zilka, M. et al (2022). Transparency, governance and regulation of algorithmic tools deployed in the criminal justice system: A UK case study(Opens in a new window). In AIES ’22: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, 880–889.
Zilka, M. et al (2022). Racial disparities in the enforcement of marijuana violations in the US(Opens in a new window). In AIES ’22: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, 130–143.
A full list is available in her website or Scholar Profile(Opens in a new window).