The reproducibility crisis has directed the AI research community toward improving documentation practices. Several studies have identified methodological issues, and in response, the most impactful venues in the field have introduced reproducibility checklists. We seek to understand whether documentation practices have changed over time by assessing all published papers at five leading AI conferences over the past decade. Seven reproducibility variables were identified, quality-assured and used to analyse 56 800 publications. Our analysis reveals that in the period 2014 to 2024, documentation practices have improved; papers sharing both code and data increased nearly sixfold, from 11% to 64% Building on empirical reproducibility rates from a prior study, we estimate - inferred from documentation practices, not direct testing - that reproducibility increased from 28% in 2014 to 64% in 2024. Improvements in documentation practices predate the introduction of reproducibility checklists, suggesting these changes reflect a broader movement toward open science rather than a direct response to formal requirements.

翻译：可重复性危机引导人工智能研究社区改进文档实践。多项研究已发现方法论问题，该领域最具影响力的学术会议为此引入了可重复性检查清单。我们通过评估过去十年五大顶级AI会议的所有发表论文，试图了解文档实践是否随时间推移发生了变化。我们识别并质量验证了七个可重复性变量，用于分析56800篇出版物。分析显示，2014年至2024年间，文档实践有所改善；同时共享代码和数据的论文比例从11%增至64%，增长了近六倍。基于先前研究的经验可重复性率，我们估计——根据文档实践推断而非直接测试——可重复性从2014年的28%上升至2024年的64%。文档实践的改善先于可重复性检查清单的引入，表明这些变化反映了更广泛的开放科学运动趋势，而非对正式要求的直接响应。