Dr. Sayash Kapoor
Princeton AI Researcher
Verified Proof of Work Artifacts
2 items catalogedEach artifact below represents an authenticated research publication, production code repository, or technical architectural framework directly authored or co-created by Dr. Sayash Kapoor. Every entry undergoes editorial source verification.
Leakage and the Reproducibility Crisis in Machine-Learning-Based Science
Published a comprehensive meta-analysis of machine learning scientific literature discovering data leakage across 329 papers spanning 17 fields, establishing formal taxonomy and verification criteria to prevent train-test contamination.
Meta-analyses depend on publicly available replication code; studies with proprietary or non-shared datasets could not be independently tested.
AI Snake Oil: What Computers Can, Can't, and Shouldn't Do
Developed the analytical distinction between perception AI (generative synthesis, speech recognition) and predictive AI (predicting human recidivism, job success), providing rigorous frameworks for evaluating claims of AI accuracy in business and governance.
Practical applications in enterprise require navigating existing regulatory guidelines that may lag behind empirical machine learning consensus.