  While machine learning models have achieved unprecedented success in
real-world applications, they might make biased/unfair decisions for specific
demographic groups and hence result in discriminative outcomes. Although
research efforts have been devoted to measuring and mitigating bias, they
mainly study bias from the result-oriented perspective while neglecting the
bias encoded in the decision-making procedure. This results in their inability
to capture procedure-oriented bias, which therefore limits the ability to have
a fully debiasing method. Fortunately, with the rapid development of
explainable machine learning, explanations for predictions are now available to
gain insights into the procedure. In this work, we bridge the gap between
fairness and explainability by presenting a novel perspective of
procedure-oriented fairness based on explanations. We identify the
procedure-based bias by measuring the gap of explanation quality between
different groups with Ratio-based and Value-based Explanation Fairness. The new
metrics further motivate us to design an optimization objective to mitigate the
procedure-based bias where we observe that it will also mitigate bias from the
prediction. Based on our designed optimization objective, we propose a
Comprehensive Fairness Algorithm (CFA), which simultaneously fulfills multiple
objectives - improving traditional fairness, satisfying explanation fairness,
and maintaining the utility performance. Extensive experiments on real-world
datasets demonstrate the effectiveness of our proposed CFA and highlight the
importance of considering fairness from the explainability perspective. Our
code is publicly available at
https://github.com/YuyingZhao/FairExplanations-CFA .
