  Given the recent successes of Deep Learning in AI there has been increased
interest in the role and need for explanations in machine learned theories. A
distinct notion in this context is that of Michie's definition of Ultra-Strong
Machine Learning (USML). USML is demonstrated by a measurable increase in human
performance of a task following provision to the human of a symbolic machine
learned theory for task performance. A recent paper demonstrates the beneficial
effect of a machine learned logic theory for a classification task, yet no
existing work to our knowledge has examined the potential harmfulness of
machine's involvement for human comprehension during learning. This paper
investigates the explanatory effects of a machine learned theory in the context
of simple two person games and proposes a framework for identifying the
harmfulness of machine explanations based on the Cognitive Science literature.
The approach involves a cognitive window consisting of two quantifiable bounds
and it is supported by empirical evidence collected from human trials. Our
quantitative and qualitative results indicate that human learning aided by a
symbolic machine learned theory which satisfies a cognitive window has achieved
significantly higher performance than human self learning. Results also
demonstrate that human learning aided by a symbolic machine learned theory that
fails to satisfy this window leads to significantly worse performance than
unaided human learning.
