# LimeOut: An Ensemble Approach To Improve Process Fairness

arXiv: https://arxiv.org/abs/2006.10531 · HTML: https://mlsanity.com/paper/2006.10531/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jun 2020.

## Built on by (most cited first, 11 of 11)

- [On Locality of Local Explanation Models](https://mlsanity.com/paper/2106.14648/) (2021)
- [Explanations, Fairness, and Appropriate Reliance in Human-AI Decision-Making](https://mlsanity.com/paper/2209.11812/) (2022)
- [A Critical Survey on Fairness Benefits of Explainable AI](https://mlsanity.com/paper/2310.13007/) (2023)
- [Survey on Fairness Notions and Related Tensions](https://mlsanity.com/paper/2209.13012/) (2022)
- [Reducing Unintended Bias of ML Models on Tabular and Textual Data](https://mlsanity.com/paper/2108.02662/) (2021)
- [A statistical approach to detect sensitive features in a group fairness setting](https://mlsanity.com/paper/2305.06994/) (2023)
- [Explanations as Bias Detectors: A Critical Study of Local Post-hoc XAI Methods for Fairness Exploration](https://mlsanity.com/paper/2505.00802/) (2025)
- [RAGUEL: Recourse-Aware Group Unfairness Elimination](https://mlsanity.com/paper/2208.14175/) (2022)
- [Fair Feature Importance Scores for Interpreting Tree-Based Methods and Surrogates](https://mlsanity.com/paper/2310.04352/) (2023)
- [Bridging Fairness and Explainability: Can Input-Based Explanations Promote Fairness in Hate Speech Detection?](https://mlsanity.com/paper/2509.22291/) (2025)
- [Fair Feature Importance Scores via Feature Occlusion and Permutation](https://mlsanity.com/paper/2602.09196/) (2026)

## Abstract (arXiv)

Artificial Intelligence and Machine Learning are becoming increasingly present in several aspects of human life, especially, those dealing with decision making. Many of these algorithmic decisions are taken without human supervision and through decision making processes that are not transparent. This raises concerns regarding the potential bias of these processes towards certain groups of society, which may entail unfair results and, possibly, violations of human rights. Dealing with such biased models is one of the major concerns to maintain the public trust. In this paper, we address the question of process or procedural fairness. More precisely, we consider the problem of making classifiers fairer by reducing their dependence on sensitive features while increasing (or, at least, maintaining) their accuracy. To achieve both, we draw inspiration from "dropout" techniques in neural based approaches, and propose a framework that relies on "feature drop-out" to tackle process fairness. We make use of "LIME Explanations" to assess a classifier's fairness and to determine the sensitive features to remove. This produces a pool of classifiers (through feature dropout) whose ensemble is shown empirically to be less dependent on sensitive features, and with improved or no impact on accuracy.