Dr. Arvind Narayanan
Professor of Computer Science
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. Arvind Narayanan. Every entry undergoes editorial source verification.
Evaluating the Fairness and Accuracy of Algorithmic Risk Assessment Instruments
Authored the classic tutorial and survey demonstrating the mathematical impossibility of satisfying three common notions of algorithmic fairness simultaneously, proving the need for explicit ethical trade-offs in automated scoring.
Mathematical trade-off proofs assume stationary data distributions and cannot resolve normative political disagreements regarding priority.
De-anonymization and Privacy Risks in Large-Scale Data Platforms
Proved that high-dimensional datasets (Netflix Prize, social graphs) can be re-identified with high probability using sparse auxiliary information, refuting the concept of 'anonymized' behavioral data in machine learning training sets.
Modern differential privacy mechanisms mitigate some vulnerability but introduce bounded utility trade-offs in downstream model training.