Whether you know it or not, you’re feeding artificial intelligence algorithms. Companies, governments, and universities around the world train machine learning software on unsuspecting citizens’ medical records, shopping history, and social media use. Sometimes the goal is to draw scientific insights, and other times it’s to keep tabs on suspicious individuals. Even AI models that abstract from data to draw conclusions about people in general can be prodded in such a way that individual records fed into them can be reconstructed. Anonymity dissolves.
To restore some amount of privacy, recent legislation such as Europe’s General Data Protection Regulation and the California Consumer Privacy Act provides a right to be forgotten. But making a trained AI model forget you often requires retraining it from scratch with all the data but yours. This process that can take weeks of computation.
Two new papers offer ways to delete records from AI models more efficiently, possibly saving megawatts of energy and making compliance more attractive. “It seemed like we needed some new algorithms to make it easy for companies to actually cooperate, so they wouldn’t have an excuse to not follow these rules,” said Melody Guan, a computer scientist at Stanford and co-author of the first paper.