  Systems that offer continuous model monitoring have emerged in response to
(1) well-documented failures of deployed Machine Learning (ML) and Artificial
Intelligence (AI) models and (2) new regulatory requirements impacting these
models. Existing monitoring systems continuously track the performance of
deployed ML models and compute feature importance (a.k.a. explanations) for
each prediction to help developers identify the root causes of emergent model
performance problems.
  We present Quantile Demographic Drift (QDD), a novel model bias
quantification metric that uses quantile binning to measure differences in the
overall prediction distributions over subgroups. QDD is ideal for continuous
monitoring scenarios, does not suffer from the statistical limitations of
conventional threshold-based bias metrics, and does not require outcome labels
(which may not be available at runtime). We incorporate QDD into a continuous
model monitoring system, called FairCanary, that reuses existing explanations
computed for each individual prediction to quickly compute explanations for the
QDD bias metrics. This optimization makes FairCanary an order of magnitude
faster than previous work that has tried to generate feature-level bias
explanations.
