面向深度学习的公平性研究综述

深度学习是机器学习研究中的一个重要领域，它具有强大的特征提取能力，且在许多应用中表现出先进的性能，因此在工业界中被广泛应用.然而，由于训练数据标注和模型设计存在偏见，现有的研究表明深度学习在某些应用中可能会强化人类的偏见和歧视，导致决策过程中的不公平现象产生，从而对个人和社会产生潜在的负面影响.为提高深度学习的应用可靠性、推动其在公平领域的发展，针对已有的研究工作，从数据和模型2方面出发，综述了深度学习应用中的偏见来源、针对不同类型偏见的去偏方法、评估去偏效果的公平性评价指标、以及目前主流的去偏平台，最后总结现有公平性研究领域存在的开放问题以及未来的发展趋势.

Fairness Research on Deep Learning

Deep learning is an important field of machine learning research,which is widely used in industry for its powerful feature extraction capabilities and advanced performance in many applications.However,due to the bias in training data labeling and model design,research shows that deep learning may aggravate human bias and discrimination in some applications,which results in unfairness during the decision-making process,thereby will cause negative impact to both individuals and socials.To improve the reliability of deep learning and promote its development in the field of fairness,we review the sources of bias in deep learning,debiasing methods for different types biases,fairness measure metrics for measuring the effect of debiasing,and current popular debiasing platforms,based on the existing research work.In the end we explore the open issues in existing fairness research field and future development trends.