Instructive artificial intelligence (AI) for human training, assistance,
  and explainability
  We propose a novel approach to explainable AI (XAI) based on the concept of
"instruction" from neural networks. In this case study, we demonstrate how a
superhuman neural network might instruct human trainees as an alternative to
traditional approaches to XAI. Specifically, an AI examines human actions and
calculates variations on the human strategy that lead to better performance.
Experiments with a JHU/APL-developed AI player for the cooperative card game
Hanabi suggest this technique makes unique contributions to explainability
while improving human performance. One area of focus for Instructive AI is in
the significant discrepancies that can arise between a human's actual strategy
and the strategy they profess to use. This inaccurate self-assessment presents
a barrier for XAI, since explanations of an AI's strategy may not be properly
understood or implemented by human recipients. We have developed and are
testing a novel, Instructive AI approach that estimates human strategy by
observing human actions. With neural networks, this allows a direct calculation
of the changes in weights needed to improve the human strategy to better
emulate a more successful AI. Subjected to constraints (e.g. sparsity) these
weight changes can be interpreted as recommended changes to human strategy
(e.g. "value A more, and value B less"). Instruction from AI such as this
functions both to help humans perform better at tasks, but also to better
understand, anticipate, and correct the actions of an AI. Results will be
presented on AI instruction's ability to improve human decision-making and
human-AI teaming in Hanabi.
