  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.
