  Since Artificial Intelligence (AI) software uses techniques like deep
lookahead search and stochastic optimization of huge neural networks to fit
mammoth datasets, it often results in complex behavior that is difficult for
people to understand. Yet organizations are deploying AI algorithms in many
mission-critical settings. To trust their behavior, we must make AI
intelligible, either by using inherently interpretable models or by developing
new methods for explaining and controlling otherwise overwhelmingly complex
decisions using local approximation, vocabulary alignment, and interactive
explanation. This paper argues that intelligibility is essential, surveys
recent work on building such systems, and highlights key directions for
research.
