Keynote (45–60 minutes), Technical Deep Dive, or Workshop on AI Ethics and Privacy
AI researchers, policymakers, technologists, data scientists, and organizations focused on ethical AI and data privacy
Building safe artificial intelligence.
Andrew details the most important new techniques in secure, privacy-preserving, and multi-owner governed artificial intelligence. Andrew begins with a sober, up-to-date view of the current state of AI safety, user privacy, and AI governance before introducing some of the fundamental tools of technical AI safety: homomorphic encryption, secure multiparty computation, federated learning, and differential privacy.
He concludes with an exciting demo from the OpenMined open source project that illustrates how to train a deep neural network while both the training data and the model are in a safe, encrypted state during the entire process.
Andrew touches on ideas such as Differential Privacy, and Secure Multi-Party Computation, and how these ideas come into play.
Many industries are limited by regulations of private data, and in response to the need for greater privacy in AI three cutting edge techniques have been developed that have huge potential for the future of machine learning in healthcare: federated learning; differential privacy; encrypted computation. These modern privacy techniques would allow us to train our models on encrypted data from multiple sources without sharing the data.
What is Meaningful Privacy?
Andrew covers how to solve the world’s biggest privacy concerns using various state-of-the-art privacy-preserving technologies, such as privacy-preserving machine learning.
Andrew Trask is a Senior Research Scientist at DeepMind, where he focuses on the intersection of privacy and artificial intelligence, and the Founder and Leader of OpenMined, an open-source community of over 12,000 contributors building tools for privacy-preserving machine learning.
Andrew’s mission is clear: to make AI both powerful and responsible. Through OpenMined, he’s championed global collaboration to ensure that data privacy, security, and access are baked into the foundation of AI systems. His work has influenced research, policy, and enterprise adoption across industries—from finance and healthcare to government and intelligence.
An accomplished educator, Andrew is the author of Grokking Deep Learning and a widely followed AI instructor on Udacity, where he makes complex ideas accessible to learners worldwide. He also authors the influential blog I am Trask, demystifying cutting-edge AI concepts for technical and non-technical audiences alike.
As a member of the United Nations Privacy Task Force, Andrew helps shape global standards for privacy-preserving analytics, advocating for open collaboration and ethical innovation in both public and private sectors.
Andrew Trask is a Senior Research Scientist at DeepMind studying Privacy and AI and the Founder and Leader at OpenMined, an open-source community of over 12,000 researchers, engineers, and enthusiasts dedicated to making the concepts and tools necessary for privacy-preserving AI reach mainstream adoption.
Andrew has worked with hedge funds, investment banks, healthcare networks, and government intelligence clients on delivering AI solutions.
Andrew Trask is also a passionate AI teacher, with a passion for making complex ideas easy to learn. He is the author of the book “Grokking Deep Learning”, an instructor in Udacity’s Deep Learning nano degree program, and the author of the popular deep learning blog “i am trask”. He is also a member of the United Nations Privacy Task Force, raising awareness and lowering the barrier to entry for the use of privacy-preserving analytics within the public sector.
“In the 1960s, the U.S. government stepped in to create the ARPANET, democratizing access to the largest supercomputers in the country, because only a limited set of researchers had access to them. Now, the U.S. government steps in to create the NAIRR, democratizing access to the largest AI resources in the country, including the largest AI supercomputers, because only a limited set of researchers have access to them. And upon the promise of the NAIRR, the future of AI will be democratic, American innovation will flourish, and we’re honored to be a part of it.” — Andrew Trask, Executive Director of OpenMined.
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In this 16-minute presentation, Andrew Trask explains how privacy-preserving AI allows collaboration across industries without sacrificing data security—advancing a future where innovation and privacy coexist.
In Privacy-Preserving AI, Andrew Trask—Founder of OpenMined and Senior Research Scientist at DeepMind—breaks down one of the most pressing challenges in artificial intelligence: how to enable data-driven innovation while protecting individual privacy. Speaking with clarity and enthusiasm, he introduces the principles and tools of privacy-preserving machine learning, including federated learning, differential privacy, and encrypted computation.
Andrew begins by framing the problem: data fuels AI progress, but traditional approaches require centralizing vast amounts of personal and sensitive information. This model, he argues, is unsustainable in a world increasingly aware of privacy risks and regulatory constraints. The solution? Train AI models without moving or exposing the data.
Using clear analogies and visual examples, Andrew shows how OpenMined’s open-source technologies allow organizations—from hospitals to financial institutions—to collaborate securely by sharing insights, not raw data. He highlights real-world use cases where these tools have enabled progress in healthcare research, fraud detection, and humanitarian work.
Throughout the talk, he underscores that privacy and innovation are not opposing forces but complementary. By decentralizing AI development and democratizing access to secure computing tools, society can unlock unprecedented progress while preserving trust.
Andrew closes by inviting technologists, policymakers, and educators to join the OpenMined movement, emphasizing that the future of AI must be both ethical and inclusive—powered by collaboration, transparency, and respect for individual rights.
In this 41-minute talk, Andrew Trask dives deep into the technology, philosophy, and global impact of privacy-preserving AI—showing how open collaboration can make artificial intelligence more ethical, secure, and democratic.
In this full-length OpenMined Talk, Andrew Trask—Founder and Leader of OpenMined and Senior Research Scientist at DeepMind—offers a comprehensive exploration of how privacy-preserving AI can redefine global data collaboration. Speaking with contagious enthusiasm and technical clarity, he describes the technologies that allow machine learning to thrive without centralizing or exposing sensitive data.
Andrew begins by outlining the core motivation behind OpenMined: democratizing access to secure AI tools. He explains how federated learning, differential privacy, and encrypted computation can enable organizations to learn from data distributed across the world—while keeping that data safe in its original locations. His examples include healthcare networks discovering new treatments, financial institutions detecting fraud collaboratively, and governments improving public services without risking citizens’ privacy.
He then shifts to the ethical and societal implications. Andrew discusses the need for trustworthy AI ecosystems, where privacy isn’t an afterthought but a prerequisite for innovation. He makes the case that privacy-preserving AI doesn’t just protect individuals—it enables collaboration at scale, bridging competitive and regulatory divides between organizations.
Midway through, Andrew shares OpenMined’s community-driven progress: more than 12,000 contributors globally building open-source frameworks that make privacy-preserving machine learning accessible to everyone. He highlights partnerships with universities, nonprofits, and international organizations working to deploy these tools for social good.
The session closes with a forward-looking perspective: as AI reshapes every sector, privacy will determine who participates in that future. By making privacy technology open, usable, and universal, Trask envisions a world where innovation and ethics advance together.
The convergence of privacy, AI ethics, and open collaboration—how we can build powerful machine learning systems without sacrificing human rights or trust.
OpenMined is a global open-source community developing frameworks for privacy-preserving AI, making advanced tools accessible to researchers and organizations worldwide.
He envisions a democratic, open infrastructure where governments, institutions, and citizens share responsibility for the ethical evolution of AI.
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The convergence of privacy, AI ethics, and open collaboration—how we can build powerful machine learning systems without sacrificing human rights or trust.
OpenMined is a global open-source community developing frameworks for privacy-preserving AI, making advanced tools accessible to researchers and organizations worldwide.
He envisions a democratic, open infrastructure where governments, institutions, and citizens share responsibility for the ethical evolution of AI.
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