It is now well understood that machine learning models, trained on data without due care, often exhibit unfair and discriminatory behavior against certain populations. Traditional algorithmic fairness research has mainly focused on supervised learning tasks, particularly classification. While fairness in unsupervised learning has received some attention, the literature has primarily addressed fair representation learning of continuous embeddings. In this paper, we conversely focus on unsupervised learning using probabilistic graphical models with discrete latent variables. We develop a fair stochastic variational inference technique for the discrete latent variables, which is accomplished by including a fairness penalty on the variational distribution that aims to respect the principles of intersectionality, a critical lens on fairness from the legal, social science, and humanities literature, and then optimizing the variational parameters under this penalty. We first show the utility of our method in improving equity and fairness for clustering using na\"ive Bayes and Gaussian mixture models on benchmark datasets. To demonstrate the generality of our approach and its potential for real-world impact, we then develop a special-purpose graphical model for criminal justice risk assessments, and use our fairness approach to prevent the inferences from encoding unfair societal biases.

翻译：传统算法公平研究主要侧重于监督的学习任务,特别是分类。尽管不受监督的学习的公平性受到了一些关注,但文献主要涉及持续嵌入的公平代表性学习。在本文中,我们反过来侧重于使用带有离散潜伏变量的概率图形模型进行无监督的学习。我们为离散潜伏变量开发了一种公平的随机变化推导技术,其实现方式是对旨在尊重交叉性原则、从法律、社会科学和人文文献中获取公平性的关键透镜以及随后优化该刑罚下的变异参数的变异分布的公平性处罚。我们首先展示了我们在利用“动态海湾”和高比斯”的基底数据集混合模型改进组合的公平和公平性方面的实用性。为了表明我们的方法的通用性及其在现实世界影响方面的潜力,我们随后开发了一种刑事司法风险评估的特殊目的图形模型,并使用我们不公平的性别化方法来防止社会风险评估。