Summary of The Unreasonable Effectiveness Of Open Science in Ai: a Replication Study, by Odd Erik Gundersen et al.
The Unreasonable Effectiveness of Open Science in AI: A Replication Study
by Odd Erik Gundersen, Odd Cappelen, Martin Mølnå, Nicklas Grimstad Nilsen
First submitted to arxiv on: 20 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG); Software Engineering (cs.SE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The authors investigate the reproducibility crisis in AI research by systematically replicating 30 highly cited studies. They find that only 50% of the articles were reproduced to some extent, with code and data sharing being strong predictors of success. The quality of data documentation also plays a crucial role, while code documentation does not significantly impact reproducibility. The study highlights the importance of open science and proper data documentation for ensuring the reliability of AI research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Artificial intelligence research is having a reproducibility crisis! To understand how big this problem is in AI, the authors looked at 30 famous studies that used real materials when available. Sadly, only half of these studies could be replicated exactly or partially. They found that sharing code and data helps make it easier to replicate results, while poor data documentation makes it hard. Surprisingly, if the code is shared, even with some mistakes, it doesn’t matter! The study shows how important open science and good data work are for making AI research reliable. |
