  Deep learning is increasingly being used in high-stake decision making
applications that affect individual lives. However, deep learning models might
exhibit algorithmic discrimination behaviors with respect to protected groups,
potentially posing negative impacts on individuals and society. Therefore,
fairness in deep learning has attracted tremendous attention recently. We
provide a review covering recent progresses to tackle algorithmic fairness
problems of deep learning from the computational perspective. Specifically, we
show that interpretability can serve as a useful ingredient to diagnose the
reasons that lead to algorithmic discrimination. We also discuss fairness
mitigation approaches categorized according to three stages of deep learning
life-cycle, aiming to push forward the area of fairness in deep learning and
build genuinely fair and reliable deep learning systems.
