11 citations · 17 across the 9 of their papers we have counts for
5 papers · 1 filter
RelShap: Relationally Consistent Shapley Explanations
Seungeun Lee, Joao Fonseca, Julia Stoyanovich
Machine learning pipelines commonly flatten relational data into single-table representations, discarding structural constraints. Widely used Shapley value-based feature attributio…
ExplainerPFN: Towards tabular foundation models for model-free zero-shot feature importance estimations
Joao Fonseca, Julia Stoyanovich
Computing the importance of features in supervised classification tasks is critical for model interpretability. Shapley values are a widely used approach for explaining model predi…
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…
Fairness in Algorithmic Recourse Through the Lens of Substantive Equality of Opportunity
Andrew Bell, Joao Fonseca, Carlo Abrate +2
Algorithmic recourse -- providing recommendations to those affected negatively by the outcome of an algorithmic system on how they can take action and change that outcome -- has ga…
Setting the Right Expectations: Algorithmic Recourse Over Time
Joao Fonseca, Andrew Bell, Carlo Abrate +2
Algorithmic systems are often called upon to assist in high-stakes decision making. In light of this, algorithmic recourse, the principle wherein individuals should be able to take…