Towards multi-modal causability with Graph Neural Networks enabling information fusion for explainable AI


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Title: Towards multi-modal causability with Graph Neural Networks enabling information fusion for explainable AI
Authors: Holzinger Andreas, Malle Bernd, Saranti Anna, Pfeifer Bastian. (2021)
Journal: Information Fusion
Publisher: Elsevier

The authors describe a novel, holistic approach to an automated medical decision pipeline, building on state-of-the-art Machine Learning research, yet integrating the human-in-the-loop via an innovative, interactive & exploration-based explainability technique called counterfactual graphs. They outline the necessity of computing a joint multi-modal representation space in a decentralized fashion, for the reasons of scalability and performance as well as ever-evolving data protection regulations. This effort is indented as a motivation for the international research community and a launchpad for further work in the fields of multi-modal embeddings, interactive explainability, counterfactuals, causability, as well as necessary foundations for effective future human–AI interfaces.

More: https://featurecloud.eu/wp-content/uploads/2021/03/Holzinger-et-al_2021_Towards-multi-model-causability.pdf

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