A PAC Framework for Aggregating Agents' Judgments

Abstract

Specifying the objective function that an AI system should pursue can be challenging. Especially when the decisions to be made by the system have a moral component, input from multiple stakeholders is often required. We consider approaches that query them about their judgments in individual examples, and then aggregate these judgments into a general policy. We propose a formal learning-theoretic framework for this setting. We then give general results on how to translate classical results from PAC learning into results in our framework. Subsequently, we show that in some settings, better results can be obtained by working directly in our framework. Finally, we discuss how our model can be extended in a variety of ways for future research.