Beyond Accuracy-Fairness: Stop evaluating bias mitigation methods solely
  on between-group metrics
Sofie Goethals Toon Calders David Martens
Artificial Intelligence (AI) finds widespread applications across various
domains, sparking concerns about fairness in its deployment. While fairness in
AI remains a central concern, the prevailing discourse often emphasizes
outcome-based metrics without a nuanced consideration of the differential
impacts within subgroups. Bias mitigation techniques do not only affect the
ranking of pairs of instances across sensitive groups, but often also
significantly affect the ranking of instances within these groups. Such changes
are hard to explain and raise concerns regarding the validity of the
intervention. Unfortunately, these effects largely remain under the radar in
the accuracy-fairness evaluation framework that is usually applied. This paper
challenges the prevailing metrics for assessing bias mitigation techniques,
arguing that they do not take into account the changes within-groups and that
the resulting prediction labels fall short of reflecting real-world scenarios.
We propose a paradigm shift: initially, we should focus on generating the most
precise ranking for each subgroup. Following this, individuals should be chosen
from these rankings to meet both fairness standards and practical
considerations.