- : Keynote (45 min)
Challenges for Delivering Machine Learning in Health
The wealth of data availability presents new opportunities in health but also challenges. In this talk we will focus on challenges for machine learning in health: 1. Paradoxes of the Data Society, 2. Quantifying the Value of Data, 3. Privacy, loss of control, marginalization.
Each of these challenges has particular implications for machine learning. The paradoxes relate to our evolving relationship with data and our changing expectations. Quantifying value is vital for accounting for the influence of data in our new digital economies and issues of privacy and loss of control are fundamental to how our pre-existing rights evolve as the digital world encroaches more closely on the physical.
One of the goals of research community should be to provide the technological tooling to address these challenges ensure that we are empowered to avoid the pitfalls of the data driven society, allowing us to reap the benefits of machine learning in applications from personalized health to health in the developing world.
Neil Lawrence is a Professor of Machine Learning and Computational Biology at the University of Sheffield in the United Kingdom. He holds a PhD in Computer science from Cambridge University and had a postdoctoral stay with Microsoft Research Cambridge. He has served as the Chair of the Neural Information Processing Conference, the premier Machine Learning conference in the world, and was the founding editor of the Journal of Machine Learning Research Workshop and Conference Proceedings. He is a fellow of the Royal Society in the working group for machine learning. He is a frequent contributor on various scientific topics regarding machine learning, data and society for The Guardian. He is considered one of the foremost experts on probabilistic modeling of real-world phenomena, specifically using Gaussian Process models. With his group, he is leading efforts to apply machine learning techniques for healthcare purposes in the African continent in collaboration with the World Health Organization.