  Interpretable Machine Learning (IML) has become increasingly important in
many real-world applications, such as autonomous cars and medical diagnosis,
where explanations are significantly preferred to help people better understand
how machine learning systems work and further enhance their trust towards
systems. However, due to the diversified scenarios and subjective nature of
explanations, we rarely have the ground truth for benchmark evaluation in IML
on the quality of generated explanations. Having a sense of explanation quality
not only matters for assessing system boundaries, but also helps to realize the
true benefits to human users in practical settings. To benchmark the evaluation
in IML, in this article, we rigorously define the problem of evaluating
explanations, and systematically review the existing efforts from
state-of-the-arts. Specifically, we summarize three general aspects of
explanation (i.e., generalizability, fidelity and persuasibility) with formal
definitions, and respectively review the representative methodologies for each
of them under different tasks. Further, a unified evaluation framework is
designed according to the hierarchical needs from developers and end-users,
which could be easily adopted for different scenarios in practice. In the end,
open problems are discussed, and several limitations of current evaluation
techniques are raised for future explorations.
