Avoiding Resentment Via Monotonic Fairness
This addresses fairness issues in automated decision-making for affected individuals, offering a novel approach to mitigate resentment.
The paper tackles the problem of individual resentment in fair classifiers by proposing monotonically constrained machine learning models, which achieve both individual fairness and demographic balance without counter-intuitive outcomes.
Classifiers that achieve demographic balance by explicitly using protected attributes such as race or gender are often politically or culturally controversial due to their lack of individual fairness, i.e. individuals with similar qualifications will receive different outcomes. Individually and group fair decision criteria can produce counter-intuitive results, e.g. that the optimal constrained boundary may reject intuitively better candidates due to demographic imbalance in similar candidates. Both approaches can be seen as introducing individual resentment, where some individuals would have received a better outcome if they either belonged to a different demographic class and had the same qualifications, or if they remained in the same class but had objectively worse qualifications (e.g. lower test scores). We show that both forms of resentment can be avoided by using monotonically constrained machine learning models to create individually fair, demographically balanced classifiers.