Examining Axiological Assumptions in Machine Learning Publications

Yashpreet Malhotra1[0009-0009-1561-616X]
1 University of New Haven, New Haven, Connecticut 06516, USA
yashmalhotra9323@gmail.com
DOI: 10.46793/eLearning2025.148M

 

Abstract.  This paper presents a study of the values embedded within machine learning research papers. A novel annotation scheme is developed to analyze how values are represented in scholarly documents, focusing on the rationales for research projects, the emphasized attributes of those projects, and the discussion or neglect of potential negative impacts. The method- ology is applied to a corpus of influential papers from top- tier machine learning conferences. The analysis explores the relationship between these encoded values and factors such as institutional affiliations and funding sources, aiming to contribute to a more nuanced understanding of the ethical dimensions of machine learning research.

Keywords:  Machine Learning Research, Scientific Values, Ethical Analysis, Research Rationales, Negative Impacts, Institutional Affiliations, Funding Sources, scholarly discourse.

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Izvor: Proceedings of the 16th International Conference on e-Learning (ELEARNING2025)