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.

中文翻译：

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深度学习中的公平性：计算视角

深度学习越来越多地用于影响个人生活的高风险决策应用程序。然而，深度学习模型可能会表现出对受保护群体的算法歧视行为，可能对个人和社会造成负面影响。因此，深度学习中的公平性最近引起了极大的关注。我们提供了一篇综述，涵盖了从计算角度解决深度学习算法公平性问题的最新进展。具体来说，我们表明可解释性可以作为诊断导致算法歧视的原因的有用成分。我们还讨论了根据深度学习生命周期的三个阶段分类的公平缓解方法，