Coverage-based Queries: Nondiscrimination Awareness in Data Transformation

When people-related data are used in automated decision making processes, social inequities can be amplified. Thus, the development of technological solutions satisfying nondiscrimination requirements, in terms of, e.g., fairness, diversity, and coverage, is currently one of the main challenges for the data management and data analytics communities. In particular, coverage constraints guarantee that a dataset includes enough items for each (protected) category of interest, thus increasing diversity with the aim of limiting the introduction of bias during the next analytical steps. While coverage constraints have been mainly used for designing data repair solutions, in our study we investigate their effects on data processing pipelines, with a special reference to data transformation. To this aim, we first introduce coverage-based queries as a means for ensuring coverage constraint satisfaction on a selection-based query result, through rewriting. We then present two approximate algorithms for coverage-based query processing: the first, covRew, initially introduced in [3], relies on data discretization and sampling; the second, covKnn is a novel contribution and relies on a nearest neighbour approach for coverage-based query processing. The algorithms are experimentally compared with respect to efficiency and effectiveness, on a real dataset.