In standard classification problems, the assumption is that the entity making the decision (the {\em principal}) has access to {\em all} the samples. However, in many contexts, she either does not have direct access to the samples, or can inspect only a limited set of samples and does not know which are the most relevant ones. In such cases, she must rely on another party (the {\em agent}) to either provide the samples or point out the most relevant ones. If the agent has a different objective, then the principal cannot trust the submitted samples to be representative. She must set a {\em policy} for how she makes decisions, keeping in mind the agent's incentives. In this paper, we introduce a theoretical framework for this problem and provide key structural and computational results.