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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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The Coming of Age of Interpretable and Explainable Machine Learning Models.pdf - Accepted Version Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (1MB) | Preview |
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 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 |
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| 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: | 02 Mar 2024 00:50 |
| DOI or ID number: | 10.1016/j.neucom.2023.02.040 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/19961 |
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