A Bayesian Approach to Identifying Representational Errors

Understand

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.