Artificial intelligence can help generate hypotheses, analyse data and search scientific literature. It can also produce convincing errors. The scientific method remains the mechanism that separates useful suggestions from reliable knowledge.

AI is increasingly used across biology, chemistry, physics, medicine and Earth science. Its role ranges from pattern recognition to simulation, experimental design and natural-language assistance.

Prediction is not explanation

A model can predict an outcome without representing the causal mechanism that produces it. This may be sufficient for some applications, but scientific understanding often requires more: what changes the outcome, under which conditions and why?

Researchers should avoid treating feature importance or fluent model-generated narratives as causal explanations. Causal claims require designs capable of distinguishing competing mechanisms.

Data leakage creates artificial success

Machine-learning evaluation assumes the test set represents unseen information. Leakage occurs when related samples, future information or preprocessing decisions allow the model indirect access to the answer.

Random splits are sometimes inappropriate. Medical data may need patient-level separation; scientific discovery may require time-based or family-based splits; geographic applications may require testing in unseen regions.

Reproducibility requires more than code

Results depend on data versions, preprocessing, random seeds, software, hardware and model selection. Releasing code helps, but restricted data and undocumented choices can still prevent reproduction.

Useful reporting includes dataset provenance, exclusion criteria, hyperparameter decisions, uncertainty and all important evaluation attempts—not only the best result.

Benchmarks shape research behaviour

Benchmarks enable comparison, but repeated optimisation can turn the test set into an implicit training target. Progress on a benchmark may no longer reflect progress on the underlying scientific objective.

Independent datasets, prospective tests and field evaluation provide stronger evidence. NIST’s work on test, evaluation, verification and validation emphasises that assessments should be adapted to the real use and consequences of an AI system.

Uncertainty should travel with the result

A point prediction without uncertainty can invite overconfidence. Confidence intervals, calibration analysis and sensitivity tests help communicate what the evidence supports. Model uncertainty is only one part; measurement error and uncertainty in the scientific assumptions also matter.

AI can expand—not replace—scientific practice

AI can search larger spaces, detect subtle patterns and propose candidates that researchers may not have considered. But hypotheses still require tests, negative results still matter and independent replication remains essential.

The most constructive view is neither that AI automates science nor that it is merely a statistical shortcut. It is a new family of instruments. Like every scientific instrument, its value depends on calibration, appropriate use and the discipline with which its output is interpreted.

Prediction and explanation are different achievements

A model may forecast an outcome without identifying the mechanism that produces it. Science often seeks causal understanding as well as prediction. Interpretability can suggest patterns to investigate, but it does not turn correlation into cause; interventions, competing hypotheses and new observations remain necessary.

Automation can scale mistakes

AI can screen papers, generate code and propose experiments rapidly. If the data contain leakage, publication bias or measurement error, automation can reproduce the flaw across many analyses. Generated citations require source-level verification, code requires testing and model-assisted changes to inclusion criteria or statistical analysis should be documented.

Reproducibility in an adaptive system

Random seeds, training data, preprocessing, model versions, prompts, tool access and human selection decisions can all affect a result. Proprietary systems may change without notice. Reproducible work therefore archives outputs and describes the computational environment in enough detail for scrutiny.

• Declare where AI influenced review, hypotheses, code, analysis or writing.
• Keep primary evidence separate from generated interpretation.
• Validate discoveries on independent data or through new experiments.
• Report negative results, uncertainty and computational resources.

AI may change the speed and scale of science, but claims still become knowledge through observation, criticism and replication.

Further reading: NIST TEVV-Athlon framework for evaluating AI systems.