ModelGo: A Practical Tool for Machine Learning License Analysis
Pages 1158 - 1169
Abstract
Productionizing machine learning projects is inherently complex, involving a multitude of interconnected components that are assembled like LEGO blocks and evolve throughout development lifecycle. These components encompass software, databases, and models, each subject to various licenses governing their reuse and redistribution. However, existing license analysis approaches for Open Source Software (OSS) are not well-suited for this context. For instance, some projects are licensed without explicitly granting sublicensing rights, or the granted rights can be revoked, potentially exposing their derivatives to legal risks. Indeed, the analysis of licenses in machine learning projects grows significantly more intricate as it involves interactions among diverse types of licenses and licensed materials. To the best of our knowledge, no prior research has delved into the exploration of license conflicts within this domain. In this paper, we introduce ModelGo, a practical tool for auditing potential legal risks in machine learning projects to enhance compliance and fairness. With ModelGo, we present license assessment reports based on five use cases with diverse model-reusing scenarios, rendered by real-world machine learning components. Finally, we summarize the reasons behind license conflicts and provide guidelines for minimizing them. Our code is publicly available at https://github.com/Xtra-Computing/ModelGo.
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- ModelGo: A Practical Tool for Machine Learning License Analysis
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Published In
May 2024
4826 pages
ISBN:9798400701719
DOI:10.1145/3589334
- General Chairs:
- Tat-Seng Chua,
- Chong-Wah Ngo,
- Proceedings Chair:
- Roy Ka-Wei Lee,
- Program Chairs:
- Ravi Kumar,
- Hady W. Lauw
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This work is licensed under a Creative Commons Attribution-NonCommercial International 4.0 License.
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Published: 13 May 2024
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