# Fair Data Pre-Processing with Imperfect Attribute Space

arXiv: https://arxiv.org/abs/2603.26456 · HTML: https://mlsanity.com/paper/2603.26456/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 61 references that are in the map.
- First posted on arXiv 27 Mar 2026.

## Builds on (most cited first, 24 of 24)

- [Counterfactual Fairness](https://mlsanity.com/paper/1703.06856/) (2017)
- [Certifying and removing disparate impact](https://mlsanity.com/paper/1412.3756/) (2014)
- [FairGAN: Fairness-aware Generative Adversarial Networks](https://mlsanity.com/paper/1805.11202/) (2018)
- [Fair Inference On Outcomes](https://mlsanity.com/paper/1705.10378/) (2017)
- [DECAF: Generating Fair Synthetic Data Using Causally-Aware Generative Networks](https://mlsanity.com/paper/2110.12884/) (2021)
- [iFair: Learning Individually Fair Data Representations for Algorithmic Decision Making](https://mlsanity.com/paper/1806.01059/) (2018)
- [Fairness in Rankings and Recommendations: An Overview](https://mlsanity.com/paper/2104.05994/) (2021)
- [A Comprehensive Survey on Graph Reduction: Sparsification, Coarsening, and Condensation](https://mlsanity.com/paper/2402.03358/) (2024)
- [Interpretable Data-Based Explanations for Fairness Debugging](https://mlsanity.com/paper/2112.09745/) (2021)
- [Causal Feature Selection for Algorithmic Fairness](https://mlsanity.com/paper/2006.06053/) (2020)
- [PreFair: Privately Generating Justifiably Fair Synthetic Data](https://mlsanity.com/paper/2212.10310/) (2022)
- [Learning for Counterfactual Fairness from Observational Data](https://mlsanity.com/paper/2307.08232/) (2023)
- [Characterization and Learning of Causal Graphs with Small Conditioning Sets](https://mlsanity.com/paper/2301.09028/) (2023)
- [The Reduced PC-Algorithm: Improved Causal Structure Learning in Large Random Networks](https://mlsanity.com/paper/1806.06209/) (2018)
- [FairPFN: A Tabular Foundation Model for Causal Fairness](https://mlsanity.com/paper/2506.07049/) (2025)
- [OTClean: Data Cleaning for Conditional Independence Violations using Optimal Transport](https://mlsanity.com/paper/2403.02372/) (2024)
- [Interventional Fairness on Partially Known Causal Graphs: A Constrained Optimization Approach](https://mlsanity.com/paper/2401.10632/) (2024)
- [Falcon: Fair Active Learning using Multi-armed Bandits](https://mlsanity.com/paper/2401.12722/) (2024)
- [iFlipper: Label Flipping for Individual Fairness](https://mlsanity.com/paper/2209.07047/) (2022)
- [FairWASP: Fast and Optimal Fair Wasserstein Pre-processing](https://mlsanity.com/paper/2311.00109/) (2023)
- [Non-Invasive Fairness in Learning through the Lens of Data Drift](https://mlsanity.com/paper/2303.17566/) (2023)
- [Efficient Maximum Fair Clique Search over Large Networks](https://mlsanity.com/paper/2312.04088/) (2023)
- [Towards counterfactual fairness through auxiliary variables](https://mlsanity.com/paper/2412.04767/) (2024)
- [CausalPre: Scalable and Effective Data Pre-Processing for Causal Fairness](https://mlsanity.com/paper/2509.15199/) (2025)

## Abstract (arXiv)

Fair data pre-processing is a widely used strategy for mitigating bias in machine learning. A promising line of research focuses on calibrating datasets to satisfy a designed fairness policy so that sensitive attributes influence outcomes only through clearly specified legitimate causal pathways. While effective on clean and information-rich data, these methods often break down in real-world scenarios with imperfect attribute spaces, where decision-relevant factors may be deemed unusable or even missing. To address this gap, we propose LatentPre, a novel framework that enables principled and robust fair data processing in practical settings. Instead of relying solely on observed attributes, LatentPre augments the fairness policy with latent attributes that capture essential but subtle signals, enabling the framework to operate as if the attribute space were perfect. These latent attributes are strategically introduced to guarantee identifiability and are estimated using a tailored expectation-maximization paradigm. The raw data is then carefully refined to conform to this latent-augmented policy, effectively removing biased patterns while preserving justifiable ones. Extensive experiments demonstrate that LatentPre consistently achieves strong fairness-utility trade-offs across diverse scenarios, advancing practical fairness-aware data management.