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.

本文介绍了一种名为 TED（Teaching Explanations for Decisions）的实用框架，它提供了能够匹配消费者心智模型的有意义的解释，通过两个例子的演示，表明这种方法的广泛性和有效性，同时无损准确性。