Algorithmic Profiling of Job Seekers in Austria: How Austerity Politics Are Made Effective - Allhutter, Doris; Cech, Florian; Fischer, Fabian; Grill, Gabriel; Mager, Astrid (Frontiers in Big Data, 2020)
As of 2020, the Public Employment Service Austria (AMS) makes use of algorithmic profiling of job seekers to increase the efficiency of its counselling process and the effectivity of active labor market programs. Based on a statistical model of job seekers’ prospects on the labor market, the system - that has become known as the AMS algorithm - is designed to classify clients of the AMS into three categories: those with high chances to find a job within half a year, those with mediocre prospects on the job market, and those clients with a bad outlook of employment in the next two years. Depending on the category a particular job seeker is classified under they will be offered differing support in (re)entering the labor market. Based in science and technology studies, critical data studies and research on fairness, accountability and transparency of algorithmic systems, this paper examines the inherent politics of the AMS algorithm. An in-depth analysis of relevant technical documentation and policy documents investigates crucial conceptual, technical and social implications of the system. The analysis shows how the design of the algorithm is influenced by technical affordances, but also by social values, norms, and goals. A discussion of the tensions, challenges and possible biases that the system entails calls into question the objectivity and neutrality of data claims and of high hopes pinned on evidence-based decision-making. In this way, the paper sheds light on the co-production of (semi)automated managerial practices in employment agencies and the framing of unemployment under austerity politics.