Reproducibility

The piece catalogs ML pitfalls—data quality issues, data leakage, mislabels and misleading metrics—and advocates rigorous validation and reproducibility checklists (REFORMS) plus tooling (MLFlow/MLOps) to curb overfitting and unreliable real-world performance.

AI-enhanced or AI-driven quality in research papers is rising, but it’s triggering concerns about citation dynamics and peer-review integrity for scientists.