Machine learning (ML) models are becoming more deeply integrated into many products and services we use every day. This proliferation of artificial intelligence (AI)/ML technology raises a host of concerns about privacy breaches, model bias, and unauthorized use of data to train models. All of these areas point to the importance of having flexible and responsive control over the data a model is trained on. Retraining a model from scratch to remove specific data points, however, is often impractical due to the high computational and financial costs involved. Research into machine unlearning (MU) aims to develop new methods to remove data points efficiently and effectively from a model without the need for extensive retraining. In this post from the Carnegie Mellon University Software Engineering Institute, we discuss our work on machine unlearning challenges and offer recommendations for more robust evaluation methods.
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