Generating Causal Explanations for Graph Neural Networks

Wanyu Lin (Hong Kong Polytechnic University)

Friday 25th March, 2022 13:00-14:00 Zoom

Abstract

These years, we have witnessed the increasing attention of deep learning on graphs with graph neural networks (GNNs) from academia and industry. GNNs have exhibited superior performance across various disciplines, such as healthcare systems, financial systems, and social information systems. These systems are typically required to make critical decisions, such as disease diagnosis in the healthcare systems. With the global calls for accountable and ethical use of artificial intelligence (AI), model explainability has been broadly recognized as one of the fundamental principles of using machine learning technologies on decision-critical applications. However, despite their practical success, most GNNs are deployed as black boxes, lacking explicit declarative knowledge representations. The deficiency of explanations for the decisions of GNNs significantly hinders the applicability of these models in decision-critical settings, where both predictive performance and interpretability are of paramount importance. For example, medical decisions are increasingly being assisted by complex predictions that should lend themselves to be verified by human experts easily. Model explanations allow us to argue for model decisions and exhibit the situation when algorithmic decisions might be biased or discriminating. In addition, precise explanations may facilitate model debugging and error analysis, which may help decide which model would better describe the data's underlying semantics. In this seminar, we are going to unveil the inner working of GNNs from the lens of causality.

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