Managing impact of poisoned inferences on deployments of hardware to downstream consumers

Abstract

Methods and systems for managing impact of inferences provided to inference consumers on decisions made by the inference consumers are disclosed. Poisoned training data may be introduced and used to train an AI model, which may then poison the AI model and lead to poisoned inferences being provided to the inference consumers. Inference consumers may deploy hardware to customers based on the poisoned inferences. To determine whether to modify the deployed hardware, a performance cost associated with the deployed hardware may be obtained. The performance cost may indicate a deviation between operation of the deployed hardware and operation of hardware that may have been deployed if an unpoisoned inference was used. If the performance cost meets a performance cost threshold, at least one additional hardware component may be deployed to the customer.