FACT: A Diagnostic for Group Fairness Trade-offs
This work addresses fairness trade-offs in machine learning, offering a diagnostic tool for researchers and practitioners, but it is incremental as it builds on existing fairness frameworks.
The authors tackled the problem of conflicting group fairness notions and their trade-offs with predictive performance by proposing a diagnostic based on the fairness-confusion tensor, which they demonstrated on synthetic and real datasets to understand accuracy-fairness trade-offs.
Group fairness, a class of fairness notions that measure how different groups of individuals are treated differently according to their protected attributes, has been shown to conflict with one another, often with a necessary cost in loss of model's predictive performance. We propose a general diagnostic that enables systematic characterization of these trade-offs in group fairness. We observe that the majority of group fairness notions can be expressed via the fairness-confusion tensor, which is the confusion matrix split according to the protected attribute values. We frame several optimization problems that directly optimize both accuracy and fairness objectives over the elements of this tensor, which yield a general perspective for understanding multiple trade-offs including group fairness incompatibilities. It also suggests an alternate post-processing method for designing fair classifiers. On synthetic and real datasets, we demonstrate the use cases of our diagnostic, particularly on understanding the trade-off landscape between accuracy and fairness.