Machine Learning in the Wild: Early Evidence of Non-Compliant ML-Automation in Open-Source Software
arXiv:2603.29698v1 Announce Type: new Abstract: The increasing availability of Machine Learning (ML) models, particularly foundation models, enables their use across a range of downstream applications, from scenarios with missing data to safety-critical contexts. This, in principle, may contravene not only the models' terms of use, but also governmental principles and regulations. This paper presents a preliminary investigation into the use of ML models by 173 open-source projects on GitHub, spanning 16 application domains. We evaluate whether models are used to make decisions, the scope of these decisions, and whether any post-processing measures are taken to reduce the risks inherent in fully autonomous systems. Lastly, we investigate the models' compliance with established terms of use.
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Abstract:The increasing availability of Machine Learning (ML) models, particularly foundation models, enables their use across a range of downstream applications, from scenarios with missing data to safety-critical contexts. This, in principle, may contravene not only the models' terms of use, but also governmental principles and regulations. This paper presents a preliminary investigation into the use of ML models by 173 open-source projects on GitHub, spanning 16 application domains. We evaluate whether models are used to make decisions, the scope of these decisions, and whether any post-processing measures are taken to reduce the risks inherent in fully autonomous systems. Lastly, we investigate the models' compliance with established terms of use. This study lays the groundwork for defining guidelines for developers and creating analysis tools that automatically identify potential regulatory violations in the use of ML models in software systems.
Subjects:
Software Engineering (cs.SE)
Cite as: arXiv:2603.29698 [cs.SE]
(or arXiv:2603.29698v1 [cs.SE] for this version)
https://doi.org/10.48550/arXiv.2603.29698
arXiv-issued DOI via DataCite (pending registration)
Journal reference: 34th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering July 05--09, 2026 Montreal, QC, Canada
Related DOI:
https://doi.org/10.1145/3803437.3805572
DOI(s) linking to related resources
Submission history
From: Daniele Bifolco [view email] [v1] Tue, 31 Mar 2026 12:53:01 UTC (91 KB)
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