Fairness seen as global sensitivity analysis

Ensuring that a predictor is not biased against a sensitive feature is the goal of fair learning. Meanwhile, Global Sensitivity Analysis (GSA) is used in numerous contexts to monitor the influence of any feature on an output variable. We merge these two domains, Global Sensitivity Analysis and Fairness, by showing how fairness can be defined using a special framework based on Global Sensitivity Analysis and how various usual indicators are common between these two fields. We also present new Global Sensitivity Analysis indices, as well as rates of convergence, that are useful as fairness proxies.

Automated summary

This study combines Global Sensitivity Analysis and Fairness, defining fairness using a special framework based on Global Sensitivity Analysis and demonstrating common indicators between the two fields. New Global Sensitivity Analysis indices and rates of convergence are also presented as fairness proxies.