Weakly Supervised Learners for Correction of AI Errors with Provable Performance Guarantees
CoRR(2024)
摘要
We present a new methodology for handling AI errors by introducing weakly
supervised AI error correctors with a priori performance guarantees. These AI
correctors are auxiliary maps whose role is to moderate the decisions of some
previously constructed underlying classifier by either approving or rejecting
its decisions. The rejection of a decision can be used as a signal to suggest
abstaining from making a decision. A key technical focus of the work is in
providing performance guarantees for these new AI correctors through bounds on
the probabilities of incorrect decisions. These bounds are distribution
agnostic and do not rely on assumptions on the data dimension. Our empirical
example illustrates how the framework can be applied to improve the performance
of an image classifier in a challenging real-world task where training data are
scarce.
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