Project Description
The project "Fairness in Automated Decision-Making (Fair ADM)" by Prof. Dr. Frauke Kreuter, Dr. Ruben Bach, and Dr. Christoph Kern deals with discrimination and fairness of algorithm-based decision-making processes (Automated Decision-Making, ADM) in the German public sector. "While ADM systems optimize bureaucratic procedures through automation, their use also raises new social and ethical questions," says Prof. Dr. Frauke Kreuter. It is feared that ADM could increase existing social discrimination. For example, ADM systems are already being used in the U.S. to assess the risk of recidivism of defendants in the context of legal proceedings. A particularly sensitive field of application of ADM in the European context is the assessment of job seekers' chances on the labor market, e.g. for the allocation of training resources, which has recently been proposed by the Austrian Public Employment Service (AMS). There is a risk that sensitive characteristics such as gender, age, or marital status are brought into the algorithmic decision-making process and thus influence the distribution of resources. In order to shed more light on this and to empirically investigate methods to correct unfair algorithms, the project develops and evaluates an ADM based on administrative labor market data.
Contact Person(s)
Project Team
Publications
- Kuppler, M., Kern, C., Bach, R. L., Kreuter, F. (2022). From fair predictions to just decisions? Conceptualizing algorithmic fairness and distributive justice in the context of data-driven decision-making. Frontiers in Sociology. https://doi.org/10.3389/fsoc.2022.883999
- Kern, C., Bach, R. L., Mautner, H., and Kreuter, F. (2021). Fairness in Algorithmic Profiling: A German Case Study. arXiv. https://arxiv.org/abs/2108.04134.
- Kuppler, M., Kern, C., Bach, R. L., Kreuter, F. (2021). Distributive Justice and Fairness Metrics in Automated Decision-making: How Much Overlap Is There? arXiv. https://arxiv.org/abs/2105.01441.