Algorithmic fairness and privacy are essential elements of trustworthy machine learning for critical decision making processes. Fair machine learning algorithms are developed to minimize discrimination against protected groups in machine learning. This is achieved, for example, by imposing a constraint on the model to equalize its behavior across different groups. This can significantly increase the influence of some training data points on the fair model. We study how this change in influence can change the information leakage of the model about its training data. We analyze the privacy risks of statistical notions of fairness (i.e., equalized odds) through the lens of membership inference attacks: inferring whether a data point was used for training a model. We show that fairness comes at the cost of privacy. However, this privacy cost is not distributed equally: the information leakage of fair models increases significantly on the unprivileged subgroups, which suffer from the discrimination in regular models. Furthermore, the more biased the underlying data is, the higher the privacy cost of achieving fairness for the unprivileged subgroups is. We demonstrate this effect on multiple datasets and explain how fairness-aware learning impacts privacy.
翻译:公平与隐私是关键决策过程可靠机器学习的基本要素。 公平机器学习算法的开发是为了尽量减少对机器学习中受保护群体的歧视。 例如,通过限制不同群体之间行为平等的模式来实现这一点。 这可以大大增加一些培训数据点对公平模式的影响。 我们研究这种影响力变化如何改变模型对培训数据的信息泄漏。 我们通过成员推论攻击的透镜分析公平统计概念(即机会均等)的隐私风险:推断是否使用一个数据点来培训模型。 我们表明公平是以隐私为代价的。 但是,这种隐私成本分配不均匀:公平模型的信息泄漏对没有特权的分组的影响极大增加,这些分组在常规模型中受到歧视。此外,基础数据更偏颇的是,实现公平对待不受欢迎的分组的隐私成本越高。我们展示了这种影响对多个数据集的影响,并解释了公平学习隐私如何受到影响。