Ensuring fair treatment of historically disadvantaged groups of individuals by machine learning (ML) guided decision-making systems is a rapidly growing point of discussion in both academics and commercial industries. This thesis aims to investigate whether a popular recidivism prediction instrument (RPI), known as COMPAS, can be accused of being unfairly biased against African-Americans and/ or women. Furthermore, the applicability of certain bias mitigation post-processing algorithms is studied for debiasing an arbitrary probabilistic recidivism predictor. Statistically conclusive results suggest that COMPAS-scores are in fact unfairly putting African-Americans at a disadvantage. However, the results with respect to a bias against women are inconclusive. Finally, reject option based classification (RObC) proves highly effective for achieving group-based fairness optima, while preserving balanced accuracy. However, these group-based fairness measures are optimised at the expense of an arguably important fairness notion, known as calibration.

Bouman, P.C.
Erasmus School of Economics

Jansen, L.J. (2020, April 16). On Algorithmic Fairness and Bias Mitigation in Recidivism Prediction. Econometrie. Retrieved from http://hdl.handle.net/2105/51867