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Daniel Solove

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Scholarship · Privacy and discrimination

Daniel Solove (Daniel J. Solove) is a professor at the George Washington University Law School whose scholarship examines privacy, data protection and technology. His AI-related work addresses the collection of personal data, algorithmic predictions and the adequacy of privacy law for governing AI systems.[1][2]

Roles and affiliations

Solove is the Eugene L. and Barbara A. Bernard Professor of Intellectual Property and Technology Law at GW Law. He is faculty co-director of the GW Center for Law & Technology: The Bernard Center and director of the Privacy and Technology Law Program. He also founded TeachPrivacy, a privacy and cybersecurity training company.[1]

Contributions and positions

AI and privacy law

In Artificial Intelligence and Privacy (2025), Solove maps privacy problems associated with both the inputs and outputs of AI systems. He argues that AI often combines and magnifies familiar privacy problems, exposing weaknesses in existing regulatory approaches. His position is that a better-designed privacy-law framework could address much of this harm, even though existing law falls short.[2]

The article treats AI as a reason to reassess privacy regulation’s underlying assumptions. It offers a roadmap for legal reform rather than presenting existing privacy statutes as a complete solution. Solove’s analysis concerns the relationship between AI capabilities and privacy protections, including how older problems change when deployed at greater scale.[2]

Data scraping

In The Great Scrape (2025), co-authored with Woodrow Hartzog, Solove examines the extraction of personal information from the internet for AI and other uses. The authors argue that public availability should not automatically remove privacy protections. They identify tensions between scraping and principles such as consent, transparency, purpose limitation and data minimization.[3]

Their proposed response is not a blanket prohibition. They favor a public-interest approach that preserves socially valuable research and other beneficial uses while restricting harmful practices. They also propose viewing personal-data scraping as surveillance and treating protection against improper scraping as a data-security responsibility. These are the authors’ reform proposals.[3]

Algorithmic predictions

With Hideyuki Matsumi, Solove wrote The Prediction Society (2025), which examines decisions based on AI forecasts about people. The authors distinguish predictions about future behavior from inferences about existing facts. They argue that forecasts can preserve historical inequalities, resist correction before the predicted event occurs, and become self-fulfilling when decisions change the person’s opportunities.[4]

Their concern extends beyond prediction accuracy: organizations using forecasts may help determine the futures they claim to predict. The article argues that conventional correction rights and accuracy duties do not adequately address these problems and that law should treat predictions as a distinct regulatory issue.[4]

Selected works

News and coverage

References

Profile sources last reviewed September 7, 2026. Dated positions and developments are identified in the text.