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Scientists should use AI as a tool, not an oracle
How AI hype leads to flawed research that fuels more hype
Three years ago, we compiled evidence revealing that an error called leakage — the machine learning version of teaching to the test — was pervasive, affecting hundreds of papers from 17 disciplines. And if the model performs better than expected, they assume that it has discovered patterns in the data that no human could have thought of, and the myth of AI as an alien intelligence makes this explanation seem readily plausible. We doubt the avalanche of flawed research can be stopped, but if at least a fraction of AI-for-science funding were diverted to better training, critical inquiry, meta-science, reproducibility, and other quality-control efforts, the havoc can be minimized.
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