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Public-private funding models in open source software development: A case study on scikit-learn
Apr 12, 2024
Governments are increasingly funding open source software (OSS) development to address concerns regarding software security, digital sovereignty, and national competitiveness in science and innovation. While announcements of governmental funding are generally well-received by OSS developers, we still have a limited understanding of how they evaluate the relative benefits and drawbacks of such funding compared to other types of funding. This paper explores this question through a case study on scikit-learn, a Python library for machine learning, whose funding combines research grants, commercial sponsorship, community donations, and a 32 million Euro grant from France's artificial intelligence strategy. Through 25 interviews with scikit-learn's maintainers and funders, this study makes two key contributions to research and practice. First, the study contributes novel findings about the design and implementation of a public-private funding model in an OSS project. It sheds light on the respective roles that public and private funders have played in supporting scikit-learn, and the processes and governance mechanisms employed by the maintainers to balance their funders' diverse interests and to safeguard community interests. Second, it offers practical recommendations. For OSS developer communities, it illustrates the benefits of a diversified funding model for balancing the merits and drawbacks of different funding sources and mitigating dependence on single funders. For companies, it serves as a reminder that sponsoring developers or OSS projects can significantly help maintainers, who often struggle with limited resources and towering workloads. For governments, it emphasises the importance of funding the maintenance of existing OSS in addition to funding the development of new software or features. The paper concludes with suggestions for future research.
The Model Openness Framework: Promoting Completeness and Openness for Reproducibility, Transparency and Usability in AI
Mar 21, 2024
Generative AI (GAI) offers unprecedented possibilities but its commercialization has raised concerns about transparency, reproducibility, bias, and safety. Many "open-source" GAI models lack the necessary components for full understanding and reproduction, and some use restrictive licenses, a practice known as "openwashing." We propose the Model Openness Framework (MOF), a ranked classification system that rates machine learning models based on their completeness and openness, following principles of open science, open source, open data, and open access. The MOF requires specific components of the model development lifecycle to be included and released under appropriate open licenses. This framework aims to prevent misrepresentation of models claiming to be open, guide researchers and developers in providing all model components under permissive licenses, and help companies, academia, and hobbyists identify models that can be safely adopted without restrictions. Wide adoption of the MOF will foster a more open AI ecosystem, accelerating research, innovation, and adoption.