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Benchmarking and Survey of Explanation Methods for Black Box Models

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arxiv 2102.13076 v1 pith:BF52FLAY submitted 2021-02-25 cs.AI cs.CYcs.LG

Francesco Bodria , Fosca Giannotti , Riccardo Guidotti , Francesca Naretto , Dino Pedreschi , Salvatore Rinzivillo This is my paper · ORCID
classification cs.AIcs.CYcs.LG
keywords explanationmethodsexplanationsmodelsbenchmarkingadoptionartificialbiases
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The widespread adoption of black-box models in Artificial Intelligence has enhanced the need for explanation methods to reveal how these obscure models reach specific decisions. Retrieving explanations is fundamental to unveil possible biases and to resolve practical or ethical issues. Nowadays, the literature is full of methods with different explanations. We provide a categorization of explanation methods based on the type of explanation returned. We present the most recent and widely used explainers, and we show a visual comparison among explanations and a quantitative benchmarking.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. MUPAX: Multidimensional Problem Agnostic eXplainable AI

    cs.LG 2025-07 reject novelty 4.0 of 10

    MUPAX's feature importance is a weighted average of masked inputs selected for low loss, and its accuracy gains stem from using ground-truth labels during mask selection.

  2. Explaining deep neural network models for electricity price forecasting with XAI

    cs.LG 2025-06 conditional novelty 4.0 of 10

    SHAP and gradient explanations of five day-ahead electricity price forecasting DNNs reveal that the most recent price dominates forecasts, and new SSHAP aggregations help visualize these patterns.

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