Data Quality and Data Ethics: Towards a Trade-off Evaluation

In the last decades, one of the main drivers for organizational success has been data-driven decision-making: strategic decisions are based on data analysis and interpretation. In this scenario, relying on dependable results becomes imperative. Therefore we must ensure that input data have good quality and the algorithms on which the analysis is based are fair: in general, Data Quality (DQ) and Data Ethics (DE) should be guaranteed. However, maximizing DQ and DE simultaneously is non-trivial, since DQ improvement techniques can negatively affect DE and vice versa. Discovering which relationships exist between DQ and DE and thoroughly analyzing it is therefore of paramount importance. The goal of this paper is to study whether, in a given context, there is a trade-off between DQ and DE: specifically, we consider the Completeness dimension of DQ, and the Fairness dimension of DE. The results of our experiments, based on two real-world well-known datasets, provided details about this trade-off and allowed us to draw some guidelines.