Fairness in Algorithmic Profiling: A German Case Study
This addresses fairness concerns in public sector algorithmic profiling for job seekers in Germany, but it is incremental as it applies existing methods to a new dataset.
The study tackled the problem of algorithmic profiling for job seekers in Germany, evaluating models for predicting long-term unemployment with respect to performance and fairness, and found that different classification policies lead to varying fairness implications, calling for rigorous auditing before deployment.
Algorithmic profiling is increasingly used in the public sector as a means to allocate limited public resources effectively and objectively. One example is the prediction-based statistical profiling of job seekers to guide the allocation of support measures by public employment services. However, empirical evaluations of potential side-effects such as unintended discrimination and fairness concerns are rare. In this study, we compare and evaluate statistical models for predicting job seekers' risk of becoming long-term unemployed with respect to prediction performance, fairness metrics, and vulnerabilities to data analysis decisions. Focusing on Germany as a use case, we evaluate profiling models under realistic conditions by utilizing administrative data on job seekers' employment histories that are routinely collected by German public employment services. Besides showing that these data can be used to predict long-term unemployment with competitive levels of accuracy, we highlight that different classification policies have very different fairness implications. We therefore call for rigorous auditing processes before such models are put to practice.