# Augmented Fairness: An Interpretable Model Augmenting Decision-Makers' Fairness

arXiv: https://arxiv.org/abs/2011.08398 · HTML: https://mlsanity.com/paper/2011.08398/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Nov 2020.

## Built on by (most cited first, 6 of 6)

- [Algorithmic Fairness in Business Analytics: Directions for Research and Practice](https://mlsanity.com/paper/2207.10991/) (2022)
- [Learning Complementary Policies for Human-AI Teams](https://mlsanity.com/paper/2302.02944/) (2023)
- [Confounding-Robust Policy Improvement with Human-AI Teams](https://mlsanity.com/paper/2310.08824/) (2023)
- [Learning to Advise Humans in High-Stakes Settings](https://mlsanity.com/paper/2210.12849/) (2022)
- [Bridging the Gap in Hybrid Decision-Making Systems](https://mlsanity.com/paper/2409.19415/) (2024)
- [A Frank System for Co-Evolutionary Hybrid Decision-Making](https://mlsanity.com/paper/2503.06229/) (2025)

## Abstract (arXiv)

We propose a model-agnostic approach for mitigating the prediction bias of a black-box decision-maker, and in particular, a human decision-maker. Our method detects in the feature space where the black-box decision-maker is biased and replaces it with a few short decision rules, acting as a "fair surrogate". The rule-based surrogate model is trained under two objectives, predictive performance and fairness. Our model focuses on a setting that is common in practice but distinct from other literature on fairness. We only have black-box access to the model, and only a limited set of true labels can be queried under a budget constraint. We formulate a multi-objective optimization for building a surrogate model, where we simultaneously optimize for both predictive performance and bias. To train the model, we propose a novel training algorithm that combines a nondominated sorting genetic algorithm with active learning. We test our model on public datasets where we simulate various biased "black-box" classifiers (decision-makers) and apply our approach for interpretable augmented fairness.