experiment-audit

Machine learning experiment validation involves verifying the integrity of evaluation scripts, result files, and ground truth provenance to detect fraudulent reporting patterns. It ensures claims align with actual testing scope before publication.

Score normalization manipulation is a fraudulent reporting pattern where evaluation metrics are artificially adjusted. Independent cross-model review detects this by applying integrity constraints to result files and auditing experiment artifacts.

To audit ML experiment integrity before finalizing research paper claims, run an independent review of your experiment artifacts. This evaluates evaluation scripts, result files, and ground truth provenance to tag research claims with PASS, WARN, or FAIL integrity status.

Yes, you can integrate experiment result fraud detection into your research workflow pipeline. It automatically tags research claims with integrity status without blocking workflow progression, generating structured audit reports for both machine and human consumption.

Common fraudulent reporting patterns in machine learning experiments include fake ground truth, score normalization fraud, phantom unrun experiments, dead code, and overstated evaluation scope. These failure modes occur when LLM agents inadvertently produce fraudulent results during execution.