Challenges for Responsible Machine Learning in Healthcare

Abstract

Responsible or trustworthy machine learning (ML) are active fields of research, with metrics and mitigation strategies being defined for different aspects of trustworthiness such as robustness, fairness or interpretability. However, these topics are often considered in isolation and in engineered or ‘simple’ (e.g. binary classification with a single binary attribute) tasks. In this talk, Jessica Schrouff will discuss how these settings are not representative of real-world applications by focusing on the healthcare domain. Jessica will show how this domain raises new challenges and the impact of ignoring the intersection of subfields in trustworthy ML. She will finally discuss how the research community can move forward to bridge the multiple gaps between research and responsible ML in healthcare.

This seminar is being held in collaboration with the King’s Turing Network Development Award, engaging leading speakers with the Turing community at King’s and beyond.

Biography

Jessica Schrouff is a Senior Research Scientist at Google Research working on machine learning for healthcare. Before joining Google in 2019, she was a Marie Curie post-doctoral fellow at University College London (UK) and Stanford University (USA), developing machine learning techniques for neuroscience discovery and clinical predictions. Throughout her career, Jessica’s interests have focussed not only on the technical advancement of machine learning methods, but also in critical aspects of their deployment such as their credibility, fairness, robustness or interpretability. Jessica is also involved in DEI initiatives, such as Women in Machine Learning (WiML) and founded the Women in Neuroscience Repository.

How to join

Please register for the seminar in advance. This is due to limited room capacity.