# A Sequentially Fair Mechanism for Multiple Sensitive Attributes

arXiv: https://arxiv.org/abs/2309.06627 · HTML: https://mlsanity.com/paper/2309.06627/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 30 references that are in the map.
- First posted on arXiv 12 Sep 2023.

## Builds on (most cited first, 12 of 12)

- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [Retiring Adult: New Datasets for Fair Machine Learning](https://mlsanity.com/paper/2108.04884/) (2021)
- [Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness](https://mlsanity.com/paper/1711.05144/) (2017)
- [Fair Regression: Quantitative Definitions and Reduction-based Algorithms](https://mlsanity.com/paper/1905.12843/) (2019)
- [Why Is My Classifier Discriminatory?](https://mlsanity.com/paper/1805.12002/) (2018)
- [Fair Regression with Wasserstein Barycenters](https://mlsanity.com/paper/2006.07286/) (2020)
- [Fairlearn: Assessing and Improving Fairness of AI Systems](https://mlsanity.com/paper/2303.16626/) (2023)
- [Blind Justice: Fairness with Encrypted Sensitive Attributes](https://mlsanity.com/paper/1806.03281/) (2018)
- [Fairness for Image Generation with Uncertain Sensitive Attributes](https://mlsanity.com/paper/2106.12182/) (2021)
- [Fair learning with Wasserstein barycenters for non-decomposable performance measures](https://mlsanity.com/paper/2209.00427/) (2022)
- [Fairness in Multi-Task Learning via Wasserstein Barycenters](https://mlsanity.com/paper/2306.10155/) (2023)
- [Mitigating Discrimination in Insurance with Wasserstein Barycenters](https://mlsanity.com/paper/2306.12912/) (2023)

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

- [Parametric Fairness with Statistical Guarantees](https://mlsanity.com/paper/2310.20508/) (2023)
- [Measuring and Mitigating Biases in Motor Insurance Pricing](https://mlsanity.com/paper/2311.11900/) (2023)
- [Geospatial Disparities: A Case Study on Real Estate Prices in Paris](https://mlsanity.com/paper/2401.16197/) (2024)
- [Doubly-Regressing Approach for Subgroup Fairness](https://mlsanity.com/paper/2510.21091/) (2025)
- [EquiPy: Sequential Fairness using Optimal Transport in Python](https://mlsanity.com/paper/2503.09866/) (2025)
- [Penalized Fair Regression for Multiple Groups in Chronic Kidney Disease](https://mlsanity.com/paper/2512.17340/) (2025)
- [Fair regression under localized demographic parity constraints](https://mlsanity.com/paper/2603.25224/) (2026)
- [COVAriance-Induced Fairness Gap Penalty for Subgroup-Fair Clustering](https://mlsanity.com/paper/2607.18119/) (2026)
- [Multi-Sensitive Distributional Fairness: Joint Wasserstein Audits and Shapley Allocation](https://mlsanity.com/paper/2610.19252/) (2026)

## Abstract (arXiv)

In the standard use case of Algorithmic Fairness, the goal is to eliminate the relationship between a sensitive variable and a corresponding score. Throughout recent years, the scientific community has developed a host of definitions and tools to solve this task, which work well in many practical applications. However, the applicability and effectivity of these tools and definitions becomes less straightfoward in the case of multiple sensitive attributes. To tackle this issue, we propose a sequential framework, which allows to progressively achieve fairness across a set of sensitive features. We accomplish this by leveraging multi-marginal Wasserstein barycenters, which extends the standard notion of Strong Demographic Parity to the case with multiple sensitive characteristics. This method also provides a closed-form solution for the optimal, sequentially fair predictor, permitting a clear interpretation of inter-sensitive feature correlations. Our approach seamlessly extends to approximate fairness, enveloping a framework accommodating the trade-off between risk and unfairness. This extension permits a targeted prioritization of fairness improvements for a specific attribute within a set of sensitive attributes, allowing for a case specific adaptation. A data-driven estimation procedure for the derived solution is developed, and comprehensive numerical experiments are conducted on both synthetic and real datasets. Our empirical findings decisively underscore the practical efficacy of our post-processing approach in fostering fair decision-making.