AI Needs to Selectively Forget

We discovered his early on as we had to effectively maintain models.

Can AI Learn to Forget?     By Samuel Greengard  in the CACM

Communications of the ACM, April 2022, Vol. 65 No. 4, Pages 9-11   10.1145/3516514

Machine learning has emerged as a valuable tool for spotting patterns and trends that might otherwise escape humans. The technology, which can build elaborate models based on everything from personal preferences to facial recognition, is used widely to understand behavior, spot patterns and trends, and make informed predictions.

Yet for all the gains, there is also plenty of pain. A major problem associated with machine learning is that once an algorithm or model exists, expunging individual records or chunks of data is extraordinarily difficult. In most cases, it is necessary to retrain the entire model—sometimes with no assurance that that model will not continue to incorporate the suspect data in some way, says Gautam Kamath, an assistant professor in the David R. Cheriton School of Computer Science at the University of Waterloo in Canada.

The data in question may originate from system logs, images, health records, social media sites, customer relationship management (CRM) systems, legacy databases, and myriad other places. As right to be forgotten mandates appear, fueled by the European Union's General Data Privacy Regulation (GDPR) and the California Consumer Privacy Act (CCPA), organizations find themselves coping with potential minefields, including significant compliance penalties.

Not surprisingly, completely retraining models is an expensive and time-consuming process, one that may or may not address the underlying problem of making sensitive data disappear or become completely untraceable. What's more, there frequently is no way to demonstrate the retrained model has been fully corrected, and that it is entirely accurate and valid.

Enter machine unlearning. Using specialized techniques—including slicing databases into smaller chunks and adapting algorithms—it may be possible to induce selective 'amnesia' in machine learning models. The field is only beginning to take shape. "The goal is to find a way to rebuild models on the fly, rather than having to build an entirely new model every time the data changes," says Aaron Roth, a professor of computer and information science at the University of Pennsylvania.  ... '