Which action is essential to reproduce a published AI safety experiment?

• A. Reproduce results by intuition
• B. Identify and reuse the original dataset exactly
• C. Follow the exact evaluation protocol and data provenance
• D. Modify evaluation metrics to improve scores

Answer: (C)

Reproducing a published AI safety experiment relies on mirroring the exact conditions under which the original results were obtained. The evaluation protocol is the blueprint for how the model is tested, including the data splits (how data is divided into training, validation, and test sets), the precise steps and order of operations, how each metric is computed, any thresholds or stopping criteria, and the specific software, hardware, and environment settings used. Data provenance traces the life of the data itself—where it came from, how it was collected, what preprocessing or cleaning was applied, any augmentations or feature extractions, and how versions of datasets and code were managed. Keeping these elements identical ensures you’re measuring the same phenomenon in the same way, so the results are truly comparable.

If you deviate from the protocol or the provenance, even in small ways, you can alter what is being measured or how results are produced, which undermines replication. Relying on intuition can’t guarantee the same numbers or conclusions. Reusing the dataset exactly helps, but only if the evaluation protocol and data handling are also identical. Modifying evaluation metrics changes the thing being evaluated, so it doesn’t reproduce the original findings.