XpertAI: uncovering model strategies for sub-manifolds
<|reference_start|>XpertAI: uncovering model strategies for sub-manifolds: In recent years, Explainable AI (XAI) methods have facilitated profound validation and knowledge extraction from ML models. While extensively studied for classification, few XAI solutions have addressed the challenges specific to regression models. In regression, explanations need to be precisely formulated to address specific user queries (e.g.\ distinguishing between `Why is the output above 0?' and `Why is the output above 50?'). They should furthermore reflect the model's behavior on the relevant data sub-manifold. In this paper, we introduce XpertAI, a framework that disentangles the prediction strategy into multiple range-specific sub-strategies and allows the formulation of precise queries about the model (the `explanandum') as a linear combination of those sub-strategies. XpertAI is formulated generally to work alongside popular XAI attribution techniques, based on occlusion, gradient integration, or reverse propagation. Qualitative and quantitative results, demonstrate the benefits of our approach.<|reference_end|>
@article{letzgus2024xpertai:,
    title={XpertAI: uncovering model strategies for sub-manifolds},
    author={Simon Letzgus, Klaus-Robert M"uller, and Gr'egoire Montavon},
    journal={arXiv preprint arXiv:2403.07486},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.07486},
    primaryClass={cs.LG}
}