Semantic Description of Explainable Machine Learning Workflows for Improving Trust

Explainable Machine Learning comprises methods and techniques that enable users to better understand the machine learning functioning and results. This work proposes an ontology that represents explainable machine learning experiments, allowing data scientists and developers to have a holistic view,...

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Autores principales:	Patricia Inoue Nakagawa, Luís Ferreira Pires, João Luiz Rebelo Moreira, Luiz Olavo Bonino da Silva Santos, Faiza Bukhsh
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	XAI machine learning semantic web technologies ontology Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/21fbc5b1d44846b3b8fe4a7daa3cd927
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spelling	oai:doaj.org-article:21fbc5b1d44846b3b8fe4a7daa3cd9272021-11-25T16:38:28ZSemantic Description of Explainable Machine Learning Workflows for Improving Trust10.3390/app1122108042076-3417https://doaj.org/article/21fbc5b1d44846b3b8fe4a7daa3cd9272021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/22/10804https://doaj.org/toc/2076-3417Explainable Machine Learning comprises methods and techniques that enable users to better understand the machine learning functioning and results. This work proposes an ontology that represents explainable machine learning experiments, allowing data scientists and developers to have a holistic view, a better understanding of the explainable machine learning process, and to build trust. We developed the ontology by reusing an existing domain-specific ontology (ML-SCHEMA) and grounding it in the Unified Foundational Ontology (UFO), aiming at achieving interoperability. The proposed ontology is structured in three modules: (1) the general module, (2) the specific module, and (3) the explanation module. The ontology was evaluated using a case study in the scenario of the COVID-19 pandemic using healthcare data from patients, which are sensitive data. In the case study, we trained a Support Vector Machine to predict mortality of patients infected with COVID-19 and applied existing explanation methods to generate explanations from the trained model. Based on the case study, we populated the ontology and queried it to ensure that it fulfills its intended purpose and to demonstrate its suitability.Patricia Inoue NakagawaLuís Ferreira PiresJoão Luiz Rebelo MoreiraLuiz Olavo Bonino da Silva SantosFaiza BukhshMDPI AGarticleXAImachine learningsemantic web technologiesontologyTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10804, p 10804 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	XAI machine learning semantic web technologies ontology Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	XAI machine learning semantic web technologies ontology Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Patricia Inoue Nakagawa Luís Ferreira Pires João Luiz Rebelo Moreira Luiz Olavo Bonino da Silva Santos Faiza Bukhsh Semantic Description of Explainable Machine Learning Workflows for Improving Trust
description	Explainable Machine Learning comprises methods and techniques that enable users to better understand the machine learning functioning and results. This work proposes an ontology that represents explainable machine learning experiments, allowing data scientists and developers to have a holistic view, a better understanding of the explainable machine learning process, and to build trust. We developed the ontology by reusing an existing domain-specific ontology (ML-SCHEMA) and grounding it in the Unified Foundational Ontology (UFO), aiming at achieving interoperability. The proposed ontology is structured in three modules: (1) the general module, (2) the specific module, and (3) the explanation module. The ontology was evaluated using a case study in the scenario of the COVID-19 pandemic using healthcare data from patients, which are sensitive data. In the case study, we trained a Support Vector Machine to predict mortality of patients infected with COVID-19 and applied existing explanation methods to generate explanations from the trained model. Based on the case study, we populated the ontology and queried it to ensure that it fulfills its intended purpose and to demonstrate its suitability.
format	article
author	Patricia Inoue Nakagawa Luís Ferreira Pires João Luiz Rebelo Moreira Luiz Olavo Bonino da Silva Santos Faiza Bukhsh
author_facet	Patricia Inoue Nakagawa Luís Ferreira Pires João Luiz Rebelo Moreira Luiz Olavo Bonino da Silva Santos Faiza Bukhsh
author_sort	Patricia Inoue Nakagawa
title	Semantic Description of Explainable Machine Learning Workflows for Improving Trust
title_short	Semantic Description of Explainable Machine Learning Workflows for Improving Trust
title_full	Semantic Description of Explainable Machine Learning Workflows for Improving Trust
title_fullStr	Semantic Description of Explainable Machine Learning Workflows for Improving Trust
title_full_unstemmed	Semantic Description of Explainable Machine Learning Workflows for Improving Trust
title_sort	semantic description of explainable machine learning workflows for improving trust
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/21fbc5b1d44846b3b8fe4a7daa3cd927
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Semantic Description of Explainable Machine Learning Workflows for Improving Trust

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