A Perspective on Explainable Artificial Intelligence Methods: SHAP and LIME

🤖 Plain-English Summary

eXplainable artificial intelligence (XAI) methods have emerged to convert the black box of machine learning (ML) models into a more digestible form. Specifically, their outcomes in terms of model‐dependency and in the presence of collinearity among the features, relying on a case study from the biomedical domain (classification of individuals with or without myocardial infarction) are discussed.

🔑 Key Findings

• These methods help to communicate how the model works with the aim of making ML models more transparent and increasing the trust of end‐users in their output.
• SHapley Additive exPlanations (SHAP) and Local Interpretable Model Agnostic Explanation (LIME) are two widely used XAI methods, particularly with tabular data.
• In this perspective piece, the way the explainability metrics of these two methods are generated is discussed and a framework for the interpretation of their outputs, highlighting their weaknesses and strengths is proposed.

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