  AI solutions seem to appear in any and all application domains. As AI becomes
more pervasive, the importance of quality assurance increases. Unfortunately,
there is no consensus on what artificial intelligence means and interpretations
range from simple statistical analysis to sentient humanoid robots. On top of
that, quality is a notoriously hard concept to pinpoint. What does this mean
for AI quality? In this paper, we share our working definition and a pragmatic
approach to address the corresponding quality assurance with a focus on
testing. Finally, we present our ongoing work on establishing the AIQ
Meta-Testbed.
