论文标题
辩论：一种贝叶斯的贝叶斯方法
DeBayes: a Bayesian Method for Debiasing Network Embeddings
论文作者
论文摘要
随着机器学习算法越来越多地用于高影响力的自动决策，道德和越来越多的法律标准要求他们公平对待所有个人，而没有基于年龄，性别，种族或其他敏感特征的歧视。近年来，在确保公平性和减少标准机器学习设置中的偏见方面取得了很多进展。但是，对于网络嵌入，在脆弱的域中的应用程序从社交网络分析到推荐系统，当前的选项在数量和性能上仍然有限。因此，我们提出了辩论：一种概念上优雅的贝叶斯方法，能够通过使用有偏见的先验来学习伪书的嵌入。我们的实验表明，这些表示形式然后可用于执行链接预测，这在人口统计学奇偶校验和均衡机会等流行指标方面更加公平。
As machine learning algorithms are increasingly deployed for high-impact automated decision making, ethical and increasingly also legal standards demand that they treat all individuals fairly, without discrimination based on their age, gender, race or other sensitive traits. In recent years much progress has been made on ensuring fairness and reducing bias in standard machine learning settings. Yet, for network embedding, with applications in vulnerable domains ranging from social network analysis to recommender systems, current options remain limited both in number and performance. We thus propose DeBayes: a conceptually elegant Bayesian method that is capable of learning debiased embeddings by using a biased prior. Our experiments show that these representations can then be used to perform link prediction that is significantly more fair in terms of popular metrics such as demographic parity and equalized opportunity.
