Brief

This paper introduces a new concept called "ambiguous strategic classification" within the field of machine learning. It explores scenarios where regulations mandate partial disclosure of a classifier's information, leading to a learning task where the system must optimize both the classifier and the uncertainty surrounding it. The research proposes using ambiguity, allowing a system to reveal a set of possible classifiers while privately choosing which one to implement, and develops algorithms for this novel approach. AI