MeST lecture by Dr. Thomas Grote: Evidence, Uncertainty and the Integration of Machine Learning into Medicine

In this talk, I present joint work with Philipp Berens where we analyse the epistemic pitfalls which arise once clinicians deploy machine learning algorithms for medical diagnosis and treatment selection. In the recent years, we have seen many scientific breakthroughs concerning the application of machine learning within the medical domain. New stories of machine learning algorithms which either meet or surpass the benchmark of ‘expert-level accuracy’ pop up on a regular basis and get featured in high-profile scientific journals. Pertinent examples include machine learning algorithms demonstrating the ability to diagnose eye diseases or different types of skin-cancer from fundus images. That said, machine learning algorithms are plagued by epistemic constraints and it remains unclear how machine learning solutions can be translated into medical practice. We hope to lay the ground for a better alignment of human and machine intelligence by identifying epistemic desiderata which allow for successfully mitigating uncertainty from algorithmic evidence. 

Dr. Thomas Grote is a postdoctoral researcher at the Ethics and Philosophy Lab (EPL) of the Cluster of Excellence: Machine Learning: New Perspectives for Science at the University of Tübingen. His research focuses on issues related to machine learning at the intersection of epistemology and ethics.

Venue:

Tuesday, January 21, 2020, at 12:00-13:30

In room 5.0.22 at CSS, Øster Farimagsgade 5A, Building 5

Everybody is welcome!