The AI Attribution Paradox: Transparency as Social Strategy in Open-Source Software Development

TL;DR

This study investigates how open-source developers strategically disclose AI assistance in commits, revealing widespread AI use, varied attribution practices, and evolving norms that balance transparency with community reception.

Contribution

It provides the first large-scale empirical analysis of attribution strategies and community responses in AI-assisted open-source development, highlighting the strategic nature of transparency practices.

Findings

Widespread AI usage in commits (95.2%)

Explicit attribution is rare (29.5%) but increasing over time

Explicit attribution leads to modest community scrutiny

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.