Understanding the spirit of a norm: Challenges for norm‐learning agents

Abstract

Social and moral norms are a fabric for holding human societies together and helping them to function. As such they will also become a means of evaluating the performance of future human–machine systems. While machine ethics has offered various approaches to endowing machines with normative competence, from the more logic‐based to the more data‐based, none of the proposals so far have considered the challenge of capturing the “spirit of a norm,” which often eludes rigid interpretation and complicates doing the right thing. We present some paradigmatic scenarios across contexts to illustrate why the spirit of a norm can be critical to make explicit and why it exposes the inadequacies of mere data‐driven “value alignment” techniques such as reinforcement learning RL for interactive, real‐time human–robot interaction. Instead, we argue that norm learning, in particular, learning to capture the spirit of a norm, requires combining common‐sense inference‐based and data‐driven approaches.