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 at the University of Sheffield, currently on leave of absence at Amazon, Cambridge. His main technical research interest is machine learning through probabilistic models. He focuses on both the algorithmic side of these models and their application. He has a particular interest on applications in personalized health and applications in the developing world. Neil is well known for his work with Gaussian processes, and has proposed Gaussian process variants of many of the successful deep learning architectures. He is also an advocate of of the ideas behind “Open Data Science” and active in public awareness (see https://www.theguardian.com/profile/neil-lawrence) and community organization. He has been both program chair and general chair of the NIPS Conference.