Why Companies "Democratise" Artificial Intelligence: The Case of Open Source Software Donations

TL;DR

This paper investigates the motivations behind companies donating AI open source software, revealing a taxonomy of incentives and highlighting governance democratization as a key enabler for broader societal and industry impacts.

Contribution

It introduces a taxonomy of social, economic, and technological incentives for AI OSS donations and emphasizes governance democratization as a novel structural enabler.

Findings

Governance democratization facilitates external contributions and standard influence.

Individual developers play a crucial role in AI democratisation efforts.

The taxonomy aids in understanding incentives for various AI democratisation initiatives.

Abstract

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.