TED: Teaching AI to Explain its Decisions
  Artificial intelligence systems are being increasingly deployed due to their
potential to increase the efficiency, scale, consistency, fairness, and
accuracy of decisions. However, as many of these systems are opaque in their
operation, there is a growing demand for such systems to provide explanations
for their decisions. Conventional approaches to this problem attempt to expose
or discover the inner workings of a machine learning model with the hope that
the resulting explanations will be meaningful to the consumer. In contrast,
this paper suggests a new approach to this problem. It introduces a simple,
practical framework, called Teaching Explanations for Decisions (TED), that
provides meaningful explanations that match the mental model of the consumer.
We illustrate the generality and effectiveness of this approach with two
different examples, resulting in highly accurate explanations with no loss of
prediction accuracy for these two examples.
