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Understanding the Role of Objectivity in Machine Learning and Research Evaluation
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab. (Machine Learning)ORCID iD: 0000-0002-2123-8187
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.ORCID iD: 0000-0002-5582-2031
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.ORCID iD: 0000-0002-6756-0147
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.ORCID iD: 0000-0003-4029-6574
2021 (English)In: Philosophies, ISSN 2409-9287, Vol. 6, no 1, article id 22Article in journal (Refereed) Published
Abstract [en]

This article makes the case for more objectivity in Machine Learning (ML) research. Any research work that claims to hold benefits has to be scrutinized based on many parameters, such as the methodology employed, ethical considerations and its theoretical or technical contribution. We approach this discussion from a Naturalist philosophical outlook. Although every analysis may be subjective, it is important for the research community to keep vetting the research for continuous growth and to produce even better work. We suggest standardizing some of the steps in ML research in an objective way and being aware of various biases threatening objectivity. The ideal of objectivity keeps research rational since objectivity requires beliefs to be based on facts. We discuss some of the current challenges, the role of objectivity in the two elements (product and process) that are up for consideration in ML and make recommendations to support the research community.

Place, publisher, year, edition, pages
Switzerland: MDPI, 2021. Vol. 6, no 1, article id 22
Keywords [en]
objectivity, machine learning, ethics, naturalism, philosophy of science
National Category
Computer Sciences
Research subject
Machine Learning
Identifiers
URN: urn:nbn:se:ltu:diva-83814DOI: 10.3390/philosophies6010022ISI: 000635676500001Scopus ID: 2-s2.0-85112443947OAI: oai:DiVA.org:ltu-83814DiVA, id: diva2:1545437
Note

Validerad;2021;Nivå 2;2021-05-03 (johcin)

Available from: 2021-04-19 Created: 2021-04-19 Last updated: 2022-10-28Bibliographically approved

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Javed, SalehaAdewumi, OluwatosinLiwicki, FoteiniLiwicki, Marcus

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