Lisboa, PJG, Saralajew, S, Vellido, A and Villmann, T (2021) The Coming of Age of Interpretable and Explainable Machine Learning Models. In: ESANN 2021 proceedings European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning . (ESANN 2021 - European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, 06 October 2021 - 08 October 2021, Online).

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Abstract

Machine learning-based systems are now part of a wide array of real-world applications seamlessly embedded in the social realm. In the wake of this realisation, strict legal regulations for these systems are currently being developed, addressing some of the risks they may pose. This is the coming of age of the interpretability and explainability problems in machine learning-based data analysis, which can no longer be seen just as an academic research problem. In this tutorial, associated to ESANN 2021 special session on “Interpretable Models in Machine Learning and Explainable Artificial Intelligence”, we discuss explainable and interpretable machine learning as post-hoc and ante-hoc strategies to address these problems and highlight several aspects related to them, including their assessment. The contributions accepted for the session are then presented in this context

Item Type:	Conference or Workshop Item (Paper)
Subjects:	Q Science > QA Mathematics Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > T Technology (General)
Divisions:	Computer Science & Mathematics
Publisher:	ESANN Ciaco
Date Deposited:	23 Mar 2022 11:13
Last Modified:	13 Apr 2022 15:18
DOI or ID number:	10.14428/esann/2021.es2021-2
URI:	https://researchonline.ljmu.ac.uk/id/eprint/16509

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