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Explanation Multiplicity in SHAP: Characterization and Assessment
Hyunseung Hwang, Seungeun Lee, Lucas Rosenblatt +2
SHAP explanations are widely used in high-stakes settings to justify decisions, yet they can differ substantially across repeated runs, even when the model, the input instance, and…
SHAP-based Explanations are Sensitive to Feature Representation
Hyunseung Hwang, Andrew Bell, Joao Fonseca +3
Local feature-based explanations are a key component of the XAI toolkit. These explanations compute feature importance values relative to an ``interpretable'' feature representatio…
XClusters: Explainability-first Clustering
Hyunseung Hwang, Steven Euijong Whang
We study the problem of explainability-first clustering where explainability becomes a first-class citizen for clustering. Previous clustering approaches use decision trees for exp…