Medicine is driving many investigators from the machine learning community to the exciting opportunities presented by applying their methodological toolkits to improve patient care. They are inspired by the impressive successes in image analysis (e.g., in radiology, pathology and dermatology) to proceed to broad application to decision support across the time series of patient encounters with healthcare. I will examine closely some of the under-appreciated assumptions in that research/engineering agenda and how ignoring these will limit success in medical applications and conversely how these assumptions define a necessary and ambitious research program in shared human-ML decision making that cannot be addressed by increased interpretability alone.
Introduction
This conference – organized under the auspices of the Isaac Newton Institute “Mathematics of Deep Learning” Programme — brings together leading researchers along with other stakeholders in industry and society to discuss issues surrounding trustworthy artificial intelligence.
This conference will overview the state-of-the-art within the wide area of trustworthy artificial intelligence including machine learning accountability, fairness, privacy, and safety; it will overview emerging directions in trustworthy artificial intelligence, and engage with academia, industry, policy makers, and the wider public.
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