Towards a Shapley Value Graph Framework for Medical peer-influence

• URL: http://arxiv.org/abs/2112.14624v1
• Date: Wed, 29 Dec 2021 16:24:50 GMT
• Title: Towards a Shapley Value Graph Framework for Medical peer-influence
• Authors: Jamie Duell, Monika Seisenberger, Gert Aarts, Shangming Zhou and Xiuyi Fan
• Abstract summary: This paper introduces a new framework to look deeper into explanations using graph representation for feature-to-feature interactions. It aims to improve the interpretability of black-box Machine Learning (ML) models and inform intervention.
• Score: 0.9449650062296824
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: eXplainable Artificial Intelligence (XAI) is a sub-field of Artificial Intelligence (AI) that is at the forefront of AI research. In XAI feature attribution methods produce explanations in the form of feature importance. A limitation of existing feature attribution methods is that there is a lack of explanation towards the consequence of intervention. Although contribution towards a certain prediction is highlighted, the influence between features and the consequence of intervention is not addressed. The aim of this paper is to introduce a new framework to look deeper into explanations using graph representation for feature-to-feature interactions to improve the interpretability of black-box Machine Learning (ML) models and inform intervention.

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