  As post hoc explanation methods are increasingly being leveraged to explain
complex models in high-stakes settings, it becomes critical to ensure that the
quality of the resulting explanations is consistently high across various
population subgroups including the minority groups. For instance, it should not
be the case that explanations associated with instances belonging to a
particular gender subgroup (e.g., female) are less accurate than those
associated with other genders. However, there is little to no research that
assesses if there exist such group-based disparities in the quality of the
explanations output by state-of-the-art explanation methods. In this work, we
address the aforementioned gaps by initiating the study of identifying
group-based disparities in explanation quality. To this end, we first outline
the key properties which constitute explanation quality and where disparities
can be particularly problematic. We then leverage these properties to propose a
novel evaluation framework which can quantitatively measure disparities in the
quality of explanations output by state-of-the-art methods. Using this
framework, we carry out a rigorous empirical analysis to understand if and when
group-based disparities in explanation quality arise. Our results indicate that
such disparities are more likely to occur when the models being explained are
complex and highly non-linear. In addition, we also observe that certain post
hoc explanation methods (e.g., Integrated Gradients, SHAP) are more likely to
exhibit the aforementioned disparities. To the best of our knowledge, this work
is the first to highlight and study the problem of group-based disparities in
explanation quality. In doing so, our work sheds light on previously unexplored
ways in which explanation methods may introduce unfairness in real world
decision making.
