  With the increasing application of machine learning in high-stake
decision-making problems, potential algorithmic bias towards people from
certain social groups poses negative impacts on individuals and our society at
large. In the real-world scenario, many such problems involve positive and
unlabeled data such as medical diagnosis, criminal risk assessment and
recommender systems. For instance, in medical diagnosis, only the diagnosed
diseases will be recorded (positive) while others will not (unlabeled). Despite
the large amount of existing work on fairness-aware machine learning in the
(semi-)supervised and unsupervised settings, the fairness issue is largely
under-explored in the aforementioned Positive and Unlabeled Learning (PUL)
context, where it is usually more severe. In this paper, to alleviate this
tension, we propose a fairness-aware PUL method named FairPUL. In particular,
for binary classification over individuals from two populations, we aim to
achieve similar true positive rates and false positive rates in both
populations as our fairness metric. Based on the analysis of the optimal fair
classifier for PUL, we design a model-agnostic post-processing framework,
leveraging both the positive examples and unlabeled ones. Our framework is
proven to be statistically consistent in terms of both the classification error
and the fairness metric. Experiments on the synthetic and real-world data sets
demonstrate that our framework outperforms state-of-the-art in both PUL and
fair classification.
