Fairness in Deep Learning: A Computational Perspective

Fairness in deep learning has attracted tremendous attention recently, as deep learning is increasingly being used in high-stake decision making applications that affect individual lives. 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.

# Ethics and Social Impacts of AI # Social Sciences # Explainable Artificial Intelligence (XAI) # Computer Science # Privacy-Preserving Technologies in Data

Mengnan Du, Fan Yang, Na Zou, et al. (2020). "Fairness in Deep Learning: A Computational Perspective". IEEE Intelligent Systems. https://doi.org/10.1109/mis.2020.3000681