In a large-scale initiative, researchers reproduced and reviewed more than 2,200 scientific papers presented at the International Conference on Machine Learning (ICML) to assess their validity and reproducibility.
This study highlights existing gaps in data documentation and source code that make the process of reproducing results difficult for other researchers. These findings emphasize the necessity of improving scientific ethics standards and increasing transparency in the publication of artificial intelligence research.