# Fairness Shields: Safeguarding against Biased Decision Makers

arXiv: https://arxiv.org/abs/2412.11994 · HTML: https://mlsanity.com/paper/2412.11994/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 47 references that are in the map.
- First posted on arXiv 16 Dec 2024.

## Builds on (most cited first, 1 of 1)

- [A Convex Framework for Fair Regression](https://mlsanity.com/paper/1706.02409/) (2017)

## Built on by (most cited first, 5 of 5)

- [Stream-Based Monitoring of Algorithmic Fairness](https://mlsanity.com/paper/2501.18331/) (2025)
- [Monitoring of Static Fairness](https://mlsanity.com/paper/2507.03048/) (2025)
- [Algorithmic Fairness: A Runtime Perspective](https://mlsanity.com/paper/2507.20711/) (2025)
- [Energy Shields for Fairness](https://mlsanity.com/paper/2605.24926/) (2026)
- [Explainably Safe Reinforcement Learning](https://mlsanity.com/paper/2606.04634/) (2026)

## Abstract (arXiv)

As AI-based decision-makers increasingly influence human lives, it is a growing concern that their decisions are often unfair or biased with respect to people's sensitive attributes, such as gender and race. Most existing bias prevention measures provide probabilistic fairness guarantees in the long run, and it is possible that the decisions are biased on specific instances of short decision sequences. We introduce fairness shielding, where a symbolic decision-maker -- the fairness shield -- continuously monitors the sequence of decisions of another deployed black-box decision-maker, and makes interventions so that a given fairness criterion is met while the total intervention costs are minimized. We present four different algorithms for computing fairness shields, among which one guarantees fairness over fixed horizons, and three guarantee fairness periodically after fixed intervals. Given a distribution over future decisions and their intervention costs, our algorithms solve different instances of bounded-horizon optimal control problems with different levels of computational costs and optimality guarantees. Our empirical evaluation demonstrates the effectiveness of these shields in ensuring fairness while maintaining cost efficiency across various scenarios.