The explanation methods generally try to approximate the black-box behavior with an interpretable predictor, also named surrogate model. This kind of approach is the one more addressed nowadays in the XAI research field.
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May 31, 2018 · This research area tackles the important problem that complex machines and algorithms often cannot provide insights into their behavior and thought processes.
The existing explanation problems, the main strategies adopted to solve them, and the desiderata for XAI methods are presented, with references to ...
This study focuses on machine learning interpretability methods; more specifically, a literature review and taxonomy of these methods are presented.
Jan 4, 2022 · In this article, we will go to see together the main methods used for explainable AI (SHAP, LIME, Tree surrogates etc.), and their characteristics.
Example-based explanation methods select particular instances of the dataset to explain the behavior of machine learning models or to explain the underlying ...
Feb 28, 2023 · Interpretability and explainability are essential principles of machine learning model and method design and development for medicine, economics, law, and ...
The study starts by explaining the background of XAI, common definitions, and summarizing recently proposed techniques in XAI for supervised machine learning.
[PDF] Explaining Explanations: An Overview of Interpretability of Machine ... - arXiv
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Feb 3, 2019 · We discuss why current approaches to explanatory methods especially for deep neural networks are insufficient. Finally, based on our survey, we ...
An explanation usually relates the feature values of an instance to its model prediction in a humanly understandable way.