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The coming of age of interpretable and explainable machine learning models

Lisboa, P, Saralajew, S, Vellido, A, Fern´andez-Domenech, R and Villmann, T (2023) The coming of age of interpretable and explainable machine learning models. Neurocomputing, 535. pp. 25-39. ISSN 0925-2312

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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 realization, 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 concepts of interpretability and explainability in machine-learning-based data analysis, which can no longer be seen just as an academic research problem. In this paper, we discuss explainable and interpretable machine learning as post hoc and ante-hoc strategies to address regulatory restrictions and highlight several aspects related to them, including their evaluation and assessment and the legal boundaries of application.

Item Type: Article
Uncontrolled Keywords: 08 Information and Computing Sciences; 09 Engineering; 17 Psychology and Cognitive Sciences; Artificial Intelligence & Image Processing
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Computer Science & Mathematics
Publisher: Elsevier
SWORD Depositor: A Symplectic
Date Deposited: 21 Jun 2023 11:59
Last Modified: 21 Jun 2023 12:00
DOI or Identification number: 10.1016/j.neucom.2023.02.040
URI: https://researchonline.ljmu.ac.uk/id/eprint/19961

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