We are being constantly judged by automated decision systems that have been widely criticised for being discriminatory and unfair. Since an algorithm is only as good as the data it works with, biases in the data can significantly amplify unfairness issues.In this paper, we take initial steps towards integrating fairness conditions into database query processing and data management systems. Specifically, we focus on selection bias in range queries. We formally define the problem of fairness-aware range queries as obtaining a fair query which is most similar to the user's query. We propose a sub-linear time algorithm for single-predicate range queries and efficient algorithms for multi-predicate range queries. Our empirical evaluation on real and synthetic datasets confirms the effectiveness and efficiency of our proposal.

Content spread inequity is a potential unfairness issue in online social networks, disparately impacting minority groups. In this paper, we view friendship suggestion, a common feature in social network platforms, as an opportunity to achieve an equitable spread of content. In particular, we propose to suggest a subset of potential edges (currently not existing in the network but likely to be accepted) that maximizes content spread while achieving fairness. Instead of re-engineering the existing systems, our proposal builds a fairness wrapper on top of the existing friendship suggestion components. We prove the problem is NP-hard and inapproximable in polynomial time unless P = NP. Therefore, allowing relaxation of the fairness constraint, we propose an algorithm based on LP-relaxation and randomized rounding with fixed approximation ratios on fairness and content spread. We provide multiple optimizations, further improving the performance of our algorithm in practice. Besides, we propose a scalable algorithm that dynamically adds subsets of nodes, chosen via iterative sampling, and solves smaller problems corresponding to these nodes. Besides theoretical analysis, we conduct comprehensive experiments on real and synthetic data sets. Across different settings, our algorithms found solutions with near-zero unfairness while significantly increasing the content spread. Our scalable algorithm could process a graph with half a million nodes on a single machine, reducing the unfairness to around 0.0004 while lifting content spread by 43%.