  Explainable machine learning (ML) enables human learning from ML, human
appeal of automated model decisions, regulatory compliance, and security audits
of ML models. Explainable ML (i.e. explainable artificial intelligence or XAI)
has been implemented in numerous open source and commercial packages and
explainable ML is also an important, mandatory, or embedded aspect of
commercial predictive modeling in industries like financial services. However,
like many technologies, explainable ML can be misused, particularly as a faulty
safeguard for harmful black-boxes, e.g. fairwashing or scaffolding, and for
other malevolent purposes like stealing models and sensitive training data. To
promote best-practice discussions for this already in-flight technology, this
short text presents internal definitions and a few examples before covering the
proposed guidelines. This text concludes with a seemingly natural argument for
the use of interpretable models and explanatory, debugging, and disparate
impact testing methods in life- or mission-critical ML systems.
