Graph Representation Learning in Health Applications and Fairness Considerations
Recent work on neuroimaging has demonstrated significant benefits of using population graphs to capture non-imaging information in the prediction of neurodegenerative and neurodevelopmental disorders. This has been enabled by advances in the field of graph representation learning. The non-imaging attributes may contain demographic information about the individuals, but also the acquisition site, as imaging protocols and hardware might significantly differ across sites in large-scale studies. This talk will give an overview of the advances that graph representation learning has contributed to the fields of neuroimaging and connectomics in recent years. It will also discuss fairness considerations that arise when these models leverage sensitive attributes.
Ira is a Senior Researcher at DeepMind working on Machine Learning research for Life Sciences with Danielle Belgrave and the Deep Learning team. Previously, she was a senior Machine Learning Researcher with the Cortex Applied Research team at Twitter UK, focusing on real-time personalisation while she carried out research at the intersection of recommender systems and algorithmic transparency. Her exploration on algorithmic amplification of political content on Twitter was featured by the Economist and the BBC, among others.