# Fairness in Algorithmic Profiling: A German Case Study

arXiv: https://arxiv.org/abs/2108.04134 · HTML: https://mlsanity.com/paper/2108.04134/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 70 references that are in the map.
- First posted on arXiv 4 Aug 2021.

## Builds on (most cited first, 3 of 3)

- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias](https://mlsanity.com/paper/1810.01943/) (2018)
- [Distributive Justice and Fairness Metrics in Automated Decision-making: How Much Overlap Is There?](https://mlsanity.com/paper/2105.01441/) (2021)

## Built on by (most cited first, 6 of 6)

- [The Benchmarking Epistemology: Validity Theory for Evaluating Machine Learning Models](https://mlsanity.com/paper/2510.23191/) (2025)
- [One Model Many Scores: Using Multiverse Analysis to Prevent Fairness Hacking and Evaluate the Influence of Model Design Decisions](https://mlsanity.com/paper/2308.16681/) (2023)
- [Uncertainty-aware predictive modeling for fair data-driven decisions](https://mlsanity.com/paper/2211.02730/) (2022)
- [Connecting Algorithmic Fairness to Quality Dimensions in Machine Learning in Official Statistics and Survey Production](https://mlsanity.com/paper/2402.09328/) (2024)
- [Transparent and Fair Profiling in Employment Services: Evidence from Switzerland](https://mlsanity.com/paper/2509.11847/) (2025)
- [Performative Learning Theory](https://mlsanity.com/paper/2602.04402/) (2026)

## Abstract (arXiv)

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.