 Annegret Hilse/Reuters/File PhotoTHE SCOOP Researchers at Amazon Web Services have come up with new ways to scrub bad data from an AI model that can lead to bias, data privacy violations, or copyright infringement. The idea, if it works, would be a major development in allowing companies to revise models once they’ve been trained. It could also help them better comply with rules to protect private information or intellectual property, like Europe’s General Data Protection Regulation, which includes the right to be forgotten. Neural networks, like generative AI models, are trained to perform specific tasks by learning complex patterns from data. In some cases, however, developers may want to remove some data from the model if it exhibits incorrect or harmful behaviors. A company might also want to block AI from copying artists’ work, disclosing sensitive documents, or generating false information for example. But it’s difficult to remove these deleterious effects; they either have to take the model down or retrain it from scratch on better data, which is expensive. “Even with the most careful controls, when you are training models with trillions of pieces of data, there could be mistakes. So we need to be able to plan ahead to know what to do when these mistakes are revealed,” Stefano Soatto, vice president of AWS AI Labs and a computer science professor at the University of California, Los Angeles, told Semafor in an interview. “Right now, the solution is to throw everything away and learn from scratch, which is quite costly and impacts energy and the environment. It’s not just a matter of masking the result, we have to remove or disgorge the information from the train models.” Dubbed “model disgorgement,” AWS researchers have been experimenting with different computational methods to try and remove data that might lead to bias, toxicity, data privacy, or copyright infringement. They outlined different techniques in a paper published in the Proceedings of the National Academy of Sciences last month, including splitting the training data into “shards” so it’s easier to delete a specific chunk or use synthetic data. These methods have yet to be applied internally to any commercial models. Soatto said it’s “still early days” but may eventually be a feasible solution to fix issues after they’ve been deployed in the real world. |
