  Explainable Artificial Intelligence (XAI) has re-emerged in response to the
development of modern AI and ML systems. These systems are complex and
sometimes biased, but they nevertheless make decisions that impact our lives.
XAI systems are frequently algorithm-focused; starting and ending with an
algorithm that implements a basic untested idea about explainability. These
systems are often not tested to determine whether the algorithm helps users
accomplish any goals, and so their explainability remains unproven. We propose
an alternative: to start with human-focused principles for the design, testing,
and implementation of XAI systems, and implement algorithms to serve that
purpose. In this paper, we review some of the basic concepts that have been
used for user-centered XAI systems over the past 40 years of research. Based on
these, we describe the "Self-Explanation Scorecard", which can help developers
understand how they can empower users by enabling self-explanation. Finally, we
present a set of empirically-grounded, user-centered design principles that may
guide developers to create successful explainable systems.
