Beyond Banning AI: A First Look at GenAI Governance in Open Source Software Communities

2026-03-27

AI Summary

Amid the widespread adoption of generative artificial intelligence (GenAI), open-source software communities lack systematic governance frameworks, facing challenges such as increased burden in contribution review and ambiguous accountability. This study addresses this gap through a multi-stage qualitative content analysis of documentation, policies, and platform guidelines from 67 high-visibility open-source projects, offering the first systematic mapping of GenAI governance practices. Moving beyond the binary “allow-or-ban” mindset, the work proposes a three-dimensional governance orientation comprising twelve concrete strategies, constructing a structured governance taxonomy. It reveals that effective GenAI governance necessitates the integration of accountability mechanisms, verification workflows, reviewer competency, code provenance tracking, and platform infrastructure. The findings provide researchers and maintainers with a conceptual baseline and actionable guidance for governing GenAI contributions in open-source ecosystems.

Technology Category

• Philosophy and Ethics of AI: Artificial General Intelligence
• Humans and AI: Crowd Sourcing and Human Computation
• Natural Language Processing: Generation

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Security and Privacy: Data transparency and provenance

Abstract

Generative AI (GenAI) is playing an increasingly important role in open source software (OSS). Beyond completing code and documentation, GenAI is increasingly involved in issues, pull requests, code reviews, and security reports. Yet, cheaper generation does not mean cheaper review - and the resulting maintenance burden has pushed OSS projects to experiment with GenAI-specific rules in contribution guidelines, security policies, and repository instructions, even including a total ban on AI-assisted contributions. However, governing GenAI in OSS is far more than a ban-or-not question. The responses remain scattered, with neither a shared governance framework in practice nor a systematic understanding in research. Therefore, in this paper, we conduct a multi-stage analysis on various qualitative materials related to GenAI governance retrieved from 67 highly visible OSS projects. Our analysis identifies recurring concerns across contribution workflows, derives three governance orientations, and maps out 12 governance strategies and their implementation patterns. We show that governing GenAI in OSS extends well beyond banning - it requires coordinated responses across accountability, verification, review capacity, code provenance, and platform infrastructure. Overall, our work distills dispersed community practices into a structured overview, providing a conceptual baseline for researchers and a practical reference for maintainers and platform designers.

Problem

• Generative AI
• Open Source Software
• AI Governance
• Contribution Policies
• Code Provenance

Innovation

• Generative AI governance
• open source software
• contribution workflows
• governance strategies
• code provenance