Miguel Luengo-Oroz
Most boards now have an AI policy. Very few have a defensible answer to what the policy actually controls when models are deployed across operations, products, and decisions about people. The harder question is how to keep AI ambition moving without losing public trust, regulatory standing, or internal credibility when the first serious failure lands.
Miguel Luengo-Oroz is a scientist, entrepreneur and former first Chief Data Scientist at the United Nations who helps organisations build AI that holds up under ethical, regulatory, and operational scrutiny.
Full Profile
Why organisations work with Miguel Luengo-Oroz
- He held the inaugural Chief Data Scientist role at the United Nations for six years, applying AI to conflict prevention, refugee response, epidemics and human rights. That is a vantage point no consultant or academic alone can replicate.
- He runs Spotlab, an operating AI company in clinical diagnostics. His commentary on AI risk is grounded in shipping production systems, not theoretical governance.
- He has published more than 100 articles in venues including Nature and MIT Technology Review, and has briefed governments in Europe and the United States, including the Global Partnership on AI.
- He invented MalariaSpot, a crowdsourced game that turned thousands of citizens into diagnostic contributors. He is fluent in both AI ethics doctrine and what it looks like when humans and AI actually share work.
Biography highlights
- First Chief Data Scientist of the United Nations, UN Global Pulse, Executive Office of the Secretary-General, 2016 to 2022.
- Founder and CEO of Spotlab, an AI platform for clinical research and universal diagnosis.
- Professor at Universidad Politécnica de Madrid; PhD (special prize) from the same institution; additional MSc from EHESS and École Normale Supérieure, Paris.
- Obama Foundation Leader (Europe 2023), Ashoka Fellow, MIT Technology Review TR35, Fellow of the Royal Society of Arts, EU Responsible Research and Innovation Award.
- More than 100 scientific articles, policy briefs and op-eds; featured in Nature, MIT Technology Review, The Guardian and The New York Times.
- Inventor of MalariaSpot.org, a crowdsourcing video game for malaria diagnosis.
Biography
The United Nations created its first Chief Data Scientist post in 2016, and Miguel Luengo-Oroz held it for six years. The brief was to put AI and data science to work on problems that do not forgive errors: epidemic response, refugee movements, hate speech detection, conflict prevention, climate. That experience shaped a working philosophy that responsible AI is not a values statement, it is an operating discipline.
Today he runs Spotlab, an AI platform for clinical research and diagnostics, built around smartphones, AI models and 3D printing to bring quality diagnostics to diseases largely absent from the digital health economy. He is also a professor at Universidad Politécnica de Madrid, where his research spans generative AI, collective intelligence and systems biology.
His commentary draws on more than 100 publications across Nature, MIT Technology Review, The Guardian and The New York Times, and on advisory work with governments in Europe and the United States and the Global Partnership on AI. Recognitions include the Obama Foundation Leaders programme, an Ashoka fellowship, MIT TR35, the EU Responsible Research and Innovation Award, and a fellowship of the Royal Society of Arts.
For senior leaders, the value is the combination: someone who has set AI policy inside the most scrutinised public institution in the world, and who currently ships AI products into regulated medical environments. He speaks about AI risk and opportunity with the specifics of someone doing the work.
Key speaking topics
- Responsible and ethical AI
- AI governance and public-interest technology
- AI in healthcare and diagnostics
- AI for sustainable development and humanitarian response
- Generative AI: applications, risks and limits
- The future of AI policy and regulation
- Data science inside multilateral institutions
Ideal for
- Boards and executive committees setting AI governance and risk posture
- CTOs, Chief Data Officers and Chief AI Officers operationalising responsible AI
- Healthcare, life sciences and pharma leaders deploying clinical AI
- Public sector, multilateral and NGO leadership teams using AI in policy and operations
Audience outcomes
- A clear view of where AI risk actually sits in deployment, beyond policy statements
- Specific examples of AI applied to high-stakes problems inside the UN and in clinical settings
- A working framework for distinguishing responsible AI as discipline from responsible AI as branding
- Sharper questions to put to internal AI programmes, vendors and regulators
- Realistic perspective on what generative AI changes for organisations over the next two to three years
Talks
A grounded tour of where AI is delivering, where it is failing, and what leaders should be tracking next.
Key takeaways:
- Where AI is moving from pilot to operating reality, and where it is not
- The categories of AI risk that boards consistently underweight
- What the next phase of AI regulation will demand of organisations
Responsible AI seen from inside the UN: the principles, and what it takes to make them operational.
Key takeaways:
- Why responsible AI is a deployment discipline, not a values statement
- Lessons from applying AI to refugees, epidemics, hate speech and human rights
- How to embed AI ethics into product and policy decisions that ship
The state of clinical and diagnostic AI, drawn from operating Spotlab inside regulated medical environments.
Key takeaways:
- Where AI is closing real gaps in diagnostics, particularly outside high-income systems
- The integration, regulatory and trust challenges that determine whether clinical AI scales
- What healthcare leaders should expect from AI vendors in the next three years