Privileged and Unprivileged Groups: An Empirical Study on the Impact of the Age Attribute on Fairness

Recent advances in software fairness investigate bias in the treatment of different population groups, which are devised based on attributes such as gender, race and age. Groups are divided into privileged groups (favourable treatment) and unprivileged groups (unfavourable treatment). To truthfully represent the real world and to measure the degree of bias according to age (young vs. old), one needs to pick a threshold to separate those groups. In this study we investigate two popular datasets (i.e., German and Bank) and the bias observed when using every possible age threshold in order to divide the population into “young” and “old” groups, in combination with three different Machine Learning models (i.e., Logistic Regression, Decision Tree, Support Vector Machine). Our results show that age thresholds do not only impact the intensity of bias in these datasets, but also the direction (i.e., which population group receives a favourable outcome). For the two investigated datasets, we present a selection of suitable age thresholds. We also found strong and very strong correlations between the dataset bias and the respective bias of trained classification models, in 83% of the cases studied. CCS CONCEPTS • Social and professional topics → User characteristics; • General and reference → Empirical studies. ACM Reference Format: Max Hort and Federica Sarro. 2022. Privileged and Unprivileged Groups: An Empirical Study on the Impact of the Age Attribute on Fairness. In International Workshop on Equirable Data and Technology (FairWare ’22), May 9, 2022, Pittsburgh, PA, USA. ACM, New York, NY, USA, 8 pages. https://doi.org/10.1145/3524491.3527308