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Causality in Explainable AI for Medical Applications

Publication Type:

Conference/Workshop Paper


SciLifeLab Science Summit 2019


AI-based medical applications, often casualty is lacking in explainable decision making. However, as human beings, we have the basic rights to ask the explanation with possible cause and effect of a machine generated decision. The challenge for AI model is to provide a better understanding of “why” different decisions have been made and “what can be the effect”. Conse-quently, reasoning on these “cause and effect” provides expla-nations for a better prediction. Introducing this casual aspect of the predicted decision and reasoning on them will be a step forward in the intelligent health monitoring systems. Today, of-ten huge amount of data from different sources are available for efficient decision making. casualty -based AI can enhance ef-ficiency in many levels such as monitoring patent’s state, better decision-making support for the Physicians, optimization of treatment support etc. Hence, a novel casual knowledge-based inference process in AI can create values for different actors. Such casual knowledge will define casual relations and argu-ments and indicates related events. Despite the inner complex structure of AI models the challenge here is to make the end-to-end learning paradigm explainable for human understand-ing. Integrating this casual knowledge in AI will support the total decision making from possibility to probability and could certainly be helpful for areas where meaningful communica-tions and trustworthy relations between a human and artificial intelligence system is necessary such as in health domain.


author = {Shahina Begum and Shaibal Barua and Mobyen Uddin Ahmed},
title = {Causality in Explainable AI for Medical Applications },
month = {May},
year = {2019},
booktitle = {SciLifeLab Science Summit 2019},
url = {}