The AI Attribution Paradox: Transparency as Social Strategy in Open-Source Software Development
Obada Kraishan
TL;DR
This paper investigates the AI attribution paradox in open-source software, showing that AI tool usage is widespread but explicit attribution is strategically variable and highly tool-dependent. Using a large-scale GitHub data pipeline (14,300 commits, 7,393 repos, 2023–2025) and a rule-based attribution classifier, it reveals that explicit disclosure correlates with modest scrutiny while overall sentiment remains neutral, and that norms evolve rapidly yet divergently across tool ecosystems. The study contributes a theoretical framing of attribution as strategic social signaling within socio-technical systems and offers practical guidance for developers, platforms, and policymakers to balance transparency, accountability, and development efficiency during AI-enabled collaboration.
Abstract
AI coding assistants have transformed software development, raising questions about transparency and attribution practices. We examine the "AI attribution paradox": how developers strategically balance acknowledging AI assistance with managing community scrutiny. Analyzing 14,300 GitHub commits across 7,393 repositories from 2023-2025, we investigated attribution strategies and community responses across eight major AI tools. Results reveal widespread AI usage (95.2% of commits) but strategic attribution: only 29.5% employ explicit disclosure, with dramatic tool variation (Claude 80.5% versus Copilot 9.0%). Explicit attribution triggers modest scrutiny (23% more questions and 21% more comments) but tool choice matters 20-30 times more for predicting reception. Community sentiment remains neutral regardless of attribution type, suggesting curiosity rather than hostility. Temporal analyses show rapid norm evolution: explicit attribution increased from near-zero in early 2024 to 40% by late 2025, indicating community adaptation. These findings illuminate attribution as strategic communication rather than simple transparency, advancing understanding of algorithmic accountability and norm formation during technological transitions. We discuss implications for developers navigating disclosure decisions, platforms designing attribution mechanisms, and researchers studying emergent practices in AI-augmented collaborative work.