The Dynamics of Explainability: Diverse Insights from SHAP Explanations using Neighbourhoods
Published in Late-breaking work, Demos and Doctoral Consortium, colocated with The 2nd World Conference on eXplainable Artificial Intelligence, 2024
Abstract
This paper presents a discussion on utilising a dashboard tool to enhance the interpretability of SHapley Additive exPlanations (SHAP) in healthcare Artificial Intelligence (AI)-based applications. Despite SHAP’s potential to demystify an AI model’s decisions, interpreting SHAP values remains challenging, especially when considering different data neighbourhoods [1]. This issue is particularly critical in healthcare, where decision-making requires high precision and clarity. We demonstrate three use cases that can effectively demonstrate the utility of interactive neighborhood exploration. The first compares SHAP explanations in two similar patient neighbourhoods with different classifications, offering unique insights into features that influence classification changes. The second use case focuses on “feature freezing” which isolates certain features to better understand their impact. This can enable highlighting diagnostic tests considered important by a Machine Learning (ML) model for a specific population of patients (eg, patients of the same age). The final use case demonstrates the relationship between sufficient features for a given classification and the importance ranking by SHAP.
Recommended citation: Pawar, Urja and O’Reilly, Ruairi and Beder, Christian and O’Shea, Donna (2024). “The Dynamics of Explainability: Diverse Insights from SHAP Explanations using Neighbourhoods.” Late-breaking work, Demos and Doctoral Consortium, colocated with The 2nd World Conference on eXplainable Artificial Intelligence.
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