Cascaded Debiasing: Studying the Cumulative Effect of Multiple Fairness-Enhancing Interventions

Understand

Understanding the cumulative effect of multiple fairness enhancing interventions at different stages of the machine learning (ML) pipeline is a critical and underexplored facet of the fairness literature.

• Such knowledge can be valuable to data scientists/ML practitioners in designing fair ML pipelines.
• This paper takes the first step in exploring this area by undertaking an extensive empirical study comprising 60 combinations of interventions, 9 fairness metrics, 2 utility metrics (Accuracy and F1 Score) across 4 benchmark datasets.
• We quantitatively analyze the experimental data to measure the impact of multiple interventions on fairness, utility and population groups.