Machine Learning Needs Scientific Rigor: Pre-Registration and Cryptographic Accountability Come to AI

Machine learning has inherited a critical flaw from unregulated research: teams can adjust metrics and thresholds after seeing results, rendering accuracy claims non-falsifiable and essentially marketing. falsify brings pre-registration—a practice proven in medicine and psychology—to ML development, using cryptographic hashes to lock specifications before experiments run and prevent the silent metric manipulation that undermines research integrity.