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KDD 2020 Tutorial on Human-Centered Explainability for Healthcare

An ACM KDD Tutorial

KDD 2020 Tutorial on Human-Centered Explainability for Healthcare


The primary goal of this tutorial is to acknowledge the many aspects/dimensions to explainability and review the appropriate choices that depend on the persona of the consumer of the explanation


In recent years, the rapid advances in Artificial Intelligence (AI) techniques along with an ever-increasing availability of healthcare data have made many novel analyses possible. Significant successes have been observed in a wide range of tasks such as next diagnosis prediction, AKI prediction, adverse event predictions including mortality and unexpected hospital re-admissions. However, there has been limited adoption and use in clinical practice of these methods due to their black-box nature. Significant amount of research is currently focused on making such methods more interpretable or to make post-hoc explanations more accessible. However, most of this work is done at a very low level and as a result may not have direct impact at the point-of-care. This tutorial will provide an overview of the landscape of different approaches that have been developed for explainability in healthcare. Specifically, we will present the problem of explainability as it pertains to various personas involved in healthcare viz. data scientists, clinical researchers, and clinicians. We will chart out the requirements for such personas and present an overview of the different approaches that can address such needs. We will also walk-through several use-cases for such approaches. In the process, we will also demonstrate how to perform such analysis using open source tools such as the IBM AI Explainability 360 Open Source Toolkit.

Presentation Materials:

Slides are available here

Pre-recorded videos


Contact author: Prithwish Chakraborty,