# Fairness seen as Global Sensitivity Analysis

arXiv: https://arxiv.org/abs/2103.04613 · HTML: https://mlsanity.com/paper/2103.04613/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Mar 2021.

## Built on by (most cited first, 9 of 9)

- [How Biased are Your Features?: Computing Fairness Influence Functions with Global Sensitivity Analysis](https://mlsanity.com/paper/2206.00667/) (2022)
- [Robust ML Auditing using Prior Knowledge](https://mlsanity.com/paper/2505.04796/) (2025)
- [A survey of Identification and mitigation of Machine Learning algorithmic biases in Image Analysis](https://mlsanity.com/paper/2210.04491/) (2022)
- [Sample Observed Effects: Enumeration, Randomization and Generalization](https://mlsanity.com/paper/2108.04376/) (2021)
- [On Measuring Intrinsic Causal Attributions in Deep Neural Networks](https://mlsanity.com/paper/2505.09660/) (2025)
- [Marginal Fairness: Fair Decision-Making under Risk Measures](https://mlsanity.com/paper/2505.18895/) (2025)
- [The Fair Game: Auditing & Debiasing AI Algorithms Over Time](https://mlsanity.com/paper/2508.06443/) (2025)
- [Evaluating Black-Box Vulnerabilities with Wasserstein-Constrained Data Perturbations](https://mlsanity.com/paper/2603.15867/) (2026)
- [Multi-Sensitive Distributional Fairness: Joint Wasserstein Audits and Shapley Allocation](https://mlsanity.com/paper/2610.19252/) (2026)

## Abstract (arXiv)

Ensuring that a predictor is not biased against a sensible feature is the key of Fairness learning. Conversely, Global Sensitivity Analysis is used in numerous contexts to monitor the influence of any feature on an output variable. We reconcile these two domains by showing how Fairness can be seen as a special framework of 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.