  Many applications of data-driven models demand transparency of decisions,
especially in health care, criminal justice, and other high-stakes
environments. Modern trends in machine learning research have led to algorithms
that are increasingly intricate to the degree that they are considered to be
black boxes. In an effort to reduce the opacity of decisions, methods have been
proposed to construe the inner workings of such models in a
human-comprehensible manner. These post hoc techniques are described as being
universal explainers - capable of faithfully augmenting decisions with
algorithmic insight. Unfortunately, there is little agreement about what
constitutes a "good" explanation. Moreover, current methods of explanation
evaluation are derived from either subjective or proxy means. In this work, we
propose a framework for the evaluation of post hoc explainers on ground truth
that is directly derived from the additive structure of a model. We demonstrate
the efficacy of the framework in understanding explainers by evaluating popular
explainers on thousands of synthetic and several real-world tasks. The
framework unveils that explanations may be accurate but misattribute the
importance of individual features.
