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

Wenhao Yang, Runzhi He, Minghui Zhou

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.

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

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.

Paper Structure

This paper contains 30 sections, 1 figure, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. GenAI for Software Development
  4. GenAI Use and Impact in Software and OSS Collaboration
  5. OSS Contribution Governance, Maintainer Workload, and Moderation
  6. The Knowledge Gaps
  7. Methodology
  8. Research Design and Scope
  9. Corpus Construction
  10. Phase 1: broad repository screening.
  11. Phase 2: snowball expansion.
  12. Data Sources and Units of Analysis
  13. Coding Procedure
  14. Stage 1: open coding of maintainer concerns.
  15. Stage 2: project-level memoing and governance orientations.
  16. ...and 15 more sections

Figures (1)

  • Figure 1: Governance strategies mapped by functional group (rows) and governance orientation (columns). Shading intensity reflects within-orientation prevalence using four bands ($\geq$70%, 35--69%, 15--34%, and $<$15%). Percentages in parentheses show the share of cases within that orientation that exhibit the strategy, while orientation totals are reported in the column headers. Empty cells indicate that the strategy was not observed in that orientation.