AI summaryⓘ
The authors explain that AI systems that help with science learn from past research, which mostly shows positive results and often ignores studies with no significant findings (null results). This imbalance, which they call the "null result gap," can make AI scientists repeat or amplify mistakes in scientific knowledge. They estimate how much this problem exists in fields like drug discovery, psychology, and cancer biology, and show how the AI process can make it worse. The authors suggest ways to fix this, like creating databases of null results and being more careful about how AI evaluates scientific claims. They warn that without such controls, AI might speed up errors along with discoveries.
AI scientist systemsnull result gappositive publication biasscientific corpusamplification indexreplication crisistraining data biasretraction metricsscientific governanceautomated evaluation
Abstract
AI scientist systems are beginning to automate the production, evaluation, and iteration of scientific hypotheses. Their promise is speed; their risk is that speed also scales errors embedded in the scientific record. We argue that a near-term risk is corpus failure: AI scientist systems are trained on and grounded in a literature that over-represents positive results and under-represents null findings. We formalise this distortion as the null result gap, estimate it across three domains (drug discovery ~0.60, psychology ~0.56, cancer biology ~0.35), and introduce an amplification index for reasoning about how retrieval, generation, and automated evaluation can compound the raw gap. Using first-order estimates, we argue that a standard three-stage pipeline can amplify corpus distortion by a factor of 2.18x, with the conclusion unchanged under more conservative multipliers. We identify four governance failure modes: confident rediscovery, ghost evidence accumulation, replication laundering, and confidence miscalibration. We then propose three interventions: null-result databases as training infrastructure, retraction-aware evaluation metrics, and mandatory training corpus disclosure. The central takeaway is that AI scientists will not only accelerate science. Without governance, they will accelerate science's blind spots before they accelerate its discoveries.