Human Biases Cause Problems for Machines Trying To Learn Chemistry

Scientists have identified human biases in datasets used to train machine learning models for computer-aided syntheses. They found that models trained on a small randomized sample of reactions outperformed those trained on larger human-selected datasets. The results show the importance of including experimental results that people might think are unimportant when it comes to developing computer programs for chemists.

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