Abstract
Artificial intelligence (AI) systems depend on massive quantities of data, often gathered by “scraping”—the automated extraction of large amounts of data from the internet. A great deal of scraped data contains people’s personal information. This personal data provides the grist for AI tools such as facial recognition, deep fakes, and generative AI. Although scraping enables web searching, archiving of records, and meaningful scientific research, scraping for AI can also be objectionable and even harmful to individuals and society.Organizations are scraping at an escalating pace and scale, even though many privacy laws are seemingly incongruous with the practice. In this Article, we contend that scraping must undergo a serious reckoning with privacy law. Scraping violates nearly all of the key principles of privacy laws, including fairness, individual rights and control, transparency, consent, purpose specification and secondary use restrictions, data minimization, onward transfer, and data security. Scraping ignores the data protection laws built around these requirements.Scraping has evaded a reckoning with privacy law largely because scrapers act as if all publicly available data were free for the taking. But the public availability of scraped data shouldn’t give scrapers a free pass. Privacy law regularly protects publicly available data, and privacy principles are implicated even when personal data is accessible to others.This Article explores the fundamental tension between scraping and privacy law. With the zealous pursuit and astronomical growth of AI, we are in the midst of what we call the “great scrape.” There must now be a great reconciliation.
Citation and Free Download of Article at:
Solove, Daniel J. and Hartzog, Woodrow, The Great Scrape: The Clash Between Scraping and Privacy (July 03, 2024). 113 California Law Review 1521 (2025), Available at SSRN: https://ssrn.com/abstract=4884485 or http://dx.doi.org/10.2139/ssrn.4884485