Beyond Prediction Performance: How Modeling Decisions Shape Fairness Outcomes in Statistical Profiling

When machine learning systems bridge from prediction to intervention — such as in statistical profiling of job seekers — seemingly minor modeling decisions can have profound consequences for who ultimately receives support. In her talk during the workshop on Bridging Prediction and Intervention Problems in Social Systems, Frauke Kreuter (LMU Munich and University of Maryland) examined how different choices in the data science pipeline affect not just predictive accuracy, but the actual composition of individuals flagged for intervention.

Using German administrative labor market data, she presented a comparative analysis of regression and machine-learning approaches for predicting long-term unemployment risk. While the models achieve comparable performance (ROC-AUC: 0.70-0.77), they show striking disagreement in which individuals are classified as high risk, with Jaccard similarities as low as 0.45 between equally accurate models. These differences cascade through the intervention pipeline: classification thresholds, feature importance patterns, and model architectures each reshape the demographic and socioeconomic profile of those targeted for support. This work highlights a critical challenge at the prediction-intervention interface: the data we use to train accurate prediction models may be sufficient for forecasting outcomes, but the choices we make in constructing those models introduce new forms of variation that directly impact intervention allocation.