  Trained AI systems and expert decision makers can make errors that are often
difficult to identify and understand. Determining the root cause for these
errors can improve future decisions. This work presents Generative Error Model
(GEM), a generative model for inferring representational errors based on
observations of an actor's behavior (either simulated agent, robot, or human).
The model considers two sources of error: those that occur due to
representational limitations -- "blind spots" -- and non-representational
errors, such as those caused by noise in execution or systematic errors present
in the actor's policy. Disambiguating these two error types allows for targeted
refinement of the actor's policy (i.e., representational errors require
perceptual augmentation, while other errors can be reduced through methods such
as improved training or attention support). We present a Bayesian inference
algorithm for GEM and evaluate its utility in recovering representational
errors on multiple domains. Results show that our approach can recover blind
spots of both reinforcement learning agents as well as human users.
