On the Sensitivity of Reward Inference to Misspecified Human Models

Understand

Inferring reward functions from human behavior is at the center of value alignment - aligning AI objectives with what we, humans, actually want.

• But doing so relies on models of how humans behave given their objectives.
• After decades of research in cognitive science, neuroscience, and behavioral economics, obtaining accurate human models remains an open research topic.
• This begs the question: how accurate do these models need to be in order for the reward inference to be accurate? On the one hand, if small errors in the model can lead to catastrophic error in inference, the entire framework of reward learning seems ill-fated, as we will never have perfect models of human behavior.