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Machine Unlearning is a process that removes the influence of a subset of training examples from a trained machine-learning model. This process can help companies automate their decision-making pipelines with AI while also reducing the risks associated with AI such as model safety, data privacy, bias monitoring, and ethics. Additionally, Machine Unlearning can help reduce the cost of training foundational models and make incremental modifications to these models easier.
