Arvind Narayanan

Indian-American computer scientist at Princeton University, where he directs the Center for Information Technology Policy. He first became known for privacy research, notably showing with Vitaly Shmatikov (2008) that supposedly anonymous Netflix PrizeNetflix Prize, prop. n.: A 2006–2009 contest in which Netflix offered a million dollars to anyone who could predict customers' movie ratings 10 percent better than its own system. It popularized machine-learning competitions and model blending. ratings could be re-identified, and for measuring online tracking; he co-wrote textbooks on cryptocurrencies and, with Solon Barocas and Moritz Hardt, on fairness in machine learning. With Sayash Kapoor he documented widespread data leakagedata leakage, n.: A mistake in building a model that lets it peek at clues it will not have in real use, such as hints about the very answer it must predict, so it shines in testing and then disappoints. errors in machine-learning-based science (2023) and wrote 'AI Snake Oil' (2024), which separates generative AI from often-ineffective predictive AIpredictive AI, n.: AI used to forecast outcomes about people or events, such as who will repay a loan, which patient may get sicker or which machine will fail, in order to guide decisions. used to make decisions about people, and the essay AI as Normal TechnologyAI as normal technology, phr.: The view that AI, however important, will change society gradually, like electricity or the internet, through slow adoption by people and institutions, rather than as a sudden, uncontrollable superintelligence. (2025), which argues that AI's effects will unfold over decades through gradual adoption, like electricity or the internet, rather than through sudden superintelligence. He is a prominent critic of AI hypeAI hype, n.: Exaggerated claims about what AI can do or will soon do, made to attract money, attention or customers, which can mislead the public and policymakers..