The Collaborative Exoskeleton of AI Science
The intersection of AI and scientific publishing has revealed significant challenges, particularly with the accuracy of citations. AI-generated papers often include fabricated references and fail to properly identify retracted works. There is a pressing need for the adaptation of existing scientific infrastructure to better support AI applications in research.
- ▪AI-generated citations are frequently erroneous, with studies showing that about 40% are fabricated.
- ▪AI tools are citing retracted papers without proper warnings, leading to misinformation in scientific literature.
- ▪The training of AI models on compromised literature has exacerbated the issue of retractions in scholarly work.
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The Collaborative Exoskeleton of AI ScienceTim O'ReillyMay 15, 20261194ShareThere is a lot of hope that AI will advance the progress of science, but unfortunately, the collision between AI and scientific publishing has not gone well.When an AI coding agent writes code, it operates within a rich ecosystem of version control, pull requests, code review, CI/CD pipelines, dependency management, and package registries. Github wasn’t designed for AI, but it turned out to be foundational infrastructure that makes AI-assisted software development work.Science has an equivalent set of infrastructure for handling identity, provenance, integrity, and discoverability.
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