一句话：对173个GitHub开源项目开展初步实证研究，揭示ML模型自动化使用中的合规风险。
随着基础模型等机器学习模型日益普及，其被用于缺失数据到安全关键等多种下游场景，可能违反模型使用条款及政府法规。本文对GitHub上173个开源项目、16个应用领域进行初步调查，考察项目是否用ML模型做决策、决策范围，以及是否采取后处理措施以降低完全自主系统的风险，并分析模型使用是否符合既定使用条款。该研究提供了非合规ML自动化的早期证据，为制定开发者指南和构建自动识别潜在违规的分析工具奠定基础。
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 postprocessing 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.
DOI 原文 · arXiv · PDF（开放获取） · DBLP
@inproceedings{abs-2603-29698,
title = {Machine Learning in the Wild: Early Evidence of Non-Compliant ML-Automation in Open-Source Software},
author = {Zohaib Arshid and Daniele Bifolco and Fiorella Zampetti and Massimiliano Di Penta},
booktitle = {FSE},
year = {2026},
doi = {10.1145/3803437.3805572}
}