为什么企业“民主化”人工智能：开源软件捐赠的案例
Why Companies "Democratise" Artificial Intelligence: The Case of Open Source Software Donations
AI总结：
本研究采用混合方法调查了向Linux Foundation捐赠的43项AI开源软件的商业激励，提出了涵盖个人与组织的社会、经济和技术激励分类体系，强调开放治理与自下而上的推动作用，为AI民主化提供分析框架。
摘要：
当企业将人工智能（AI）开源软件（OSS）捐赠给非营利基金会或发布AI模型等时，它们声称要“民主化”AI，但这个术语意味着什么，它们为什么要这样做？随着AI对社会和经济的影响日益增长，理解AI民主化努力背后的商业激励对于确保这些努力服务于超越商业议程的更广泛利益至关重要。为此，本研究采用混合方法，调查了向Linux Foundation捐赠的43项AI OSS的商业激励。它对研究与实践均做出了贡献。它提供了一个涵盖AI民主化的个人与组织的社会、经济和技术激励的分类体系。特别是，它强调了民主化OSS项目的治理和控制权（即从单一公司转向开放治理）作为下游目标的结构性推动因素的作用，例如吸引外部贡献者、降低开发成本和影响行业标准等。此外，OSS捐赠通常由公司内部的个人开发者倡导，突显了自下而上激励对AI民主化的重要性。该分类体系为辨别其他AI民主化努力（如发布AI模型）的激励提供了框架和工具包。本文最后讨论了未来的研究方向。
英文摘要：
Companies claim to "democratise" artificial intelligence (AI) when they donate AI open source software (OSS) to non-profit foundations or release AI models, among others, but what does this term mean and why do they do it? As the impact of AI on society and the economy grows, understanding the commercial incentives behind AI democratisation efforts is crucial for ensuring these efforts serve broader interests beyond commercial agendas. Towards this end, this study employs a mixed-methods approach to investigate commercial incentives for 43 AI OSS donations to the Linux Foundation. It makes contributions to both research and practice. It contributes a taxonomy of both individual and organisational social, economic, and technological incentives for AI democratisation. In particular, it highlights the role of democratising the governance and control rights of an OSS project (i.e., from one company to open governance) as a structural enabler for downstream goals, such as attracting external contributors, reducing development costs, and influencing industry standards, among others. Furthermore, OSS donations are often championed by individual developers within companies, highlighting the importance of the bottom-up incentives for AI democratisation. The taxonomy provides a framework and toolkit for discerning incentives for other AI democratisation efforts, such as the release of AI models. The paper concludes with a discussion of future research directions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
