“[I]f the commission finds that a company trained a large language model on improperly obtained data, then it will have to delete all the information along with the products developed from the ill-gotten data.”
Cyberscoop
The Federal Trade Commission (FTC) has a powerful enforcement tool called algorithm disgorgement, which requires companies to delete products built on data that they shouldn’t have used in the first place. This enforcement strategy aims to address the common practice in the tech industry of using large amounts of data for purposes other than what it was originally obtained for, violating privacy.
The FTC has used algorithm disgorgement in five cases against tech companies since 2019, including settlements with Amazon and Cambridge Analytica. The agency sees algorithm disgorgement as a warning to other companies that mishandle user data and emphasizes that machine learning is not an excuse to break the law. The FTC’s ability to regulate AI is strengthened by the flexibility of its mandate provided by Congress. Ben Wiseman, an acting associate director at the FTC, stated that model deletion is a significant part of the agency’s enforcement strategy for AI.
However, there are challenges in implementing model deletion, as AI systems are not designed to be rolled back to certain points in time, making it difficult to disintegrate learned information. The FTC’s pursuit of model deletion depends on the facts of each case, but companies that use illegally obtained data should expect scrutiny and the possibility of data deletion.
Experts note that the FTC’s ability to bring cases calling for model deletion is currently limited, as it often relies on federal children’s privacy law. They suggest that a comprehensive federal privacy law protecting users of all ages would make it easier for the agency to bring privacy-related cases. Overall, algorithm disgorgement is seen as an effective enforcement tool because it imposes significant costs on a company’s business model, going beyond mere fines.
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