  AI-enabled capabilities are reaching the requisite level of maturity to be
deployed in the real world, yet do not always make correct or safe decisions.
One way of addressing these concerns is to leverage AI control systems
alongside and in support of human decisions, relying on the AI control system
in safe situations while calling on a human co-decider for critical situations.
We extend a methodology for adversarial explanations (AE) to state-of-the-art
reinforcement learning frameworks, including MuZero. Multiple improvements to
the base agent architecture are proposed. We demonstrate how this technology
has two applications: for intelligent decision tools and to enhance training /
learning frameworks. In a decision support context, adversarial explanations
help a user make the correct decision by highlighting those contextual factors
that would need to change for a different AI-recommended decision. As another
benefit of adversarial explanations, we show that the learned AI control system
demonstrates robustness against adversarial tampering. Additionally, we
supplement AE by introducing strategically similar autoencoders (SSAs) to help
users identify and understand all salient factors being considered by the AI
system. In a training / learning framework, this technology can improve both
the AI's decisions and explanations through human interaction. Finally, to
identify when AI decisions would most benefit from human oversight, we tie this
combined system to our prior art on statistically verified analyses of the
criticality of decisions at any point in time.
