# GFLC: Graph-based Fairness-aware Label Correction for Fair Classification

arXiv: https://arxiv.org/abs/2506.15620 · HTML: https://mlsanity.com/paper/2506.15620/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 37 references that are in the map.
- First posted on arXiv 18 Jun 2025.

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

- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Understanding over-squashing and bottlenecks on graphs via curvature](https://mlsanity.com/paper/2111.14522/) (2021)
- [Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks](https://mlsanity.com/paper/2103.14749/) (2021)
- [Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness](https://mlsanity.com/paper/1711.05144/) (2017)
- [Aequitas: A Bias and Fairness Audit Toolkit](https://mlsanity.com/paper/1811.05577/) (2018)
- [A Survey of Label-noise Representation Learning: Past, Present and Future](https://mlsanity.com/paper/2011.04406/) (2020)
- [Image Classification with Deep Learning in the Presence of Noisy Labels: A Survey](https://mlsanity.com/paper/1912.05170/) (2019)
- [Revisiting Over-smoothing and Over-squashing Using Ollivier-Ricci Curvature](https://mlsanity.com/paper/2211.15779/) (2022)
- [Fair Classification with Group-Dependent Label Noise](https://mlsanity.com/paper/2011.00379/) (2020)
- [Community Detection on Networks with Ricci Flow](https://mlsanity.com/paper/1907.03993/) (2019)
- [Noise-tolerant fair classification](https://mlsanity.com/paper/1901.10837/) (2019)
- [Turning the Tables: Biased, Imbalanced, Dynamic Tabular Datasets for ML Evaluation](https://mlsanity.com/paper/2211.13358/) (2022)
- [Fairness Evaluation in Presence of Biased Noisy Labels](https://mlsanity.com/paper/2003.13808/) (2020)
- [Characterizing Complex Networks with Forman-Ricci Curvature and Associated Geometric Flows](https://mlsanity.com/paper/1607.08654/) (2016)
- [Can Less be More? When Increasing-to-Balancing Label Noise Rates Considered Beneficial](https://mlsanity.com/paper/2107.05913/) (2021)
- [Forman-Ricci flow for change detection in large dynamic data sets](https://mlsanity.com/paper/1604.06634/) (2016)
- [Analysis of label noise in graph-based semi-supervised learning](https://mlsanity.com/paper/2009.12966/) (2020)
- [Graph Laplacian for Semi-Supervised Learning](https://mlsanity.com/paper/2301.04956/) (2023)
- [Leveraging an Alignment Set in Tackling Instance-Dependent Label Noise](https://mlsanity.com/paper/2307.04868/) (2023)
- [Aequitas Flow: Streamlining Fair ML Experimentation](https://mlsanity.com/paper/2405.05809/) (2024)
- [Systematic analysis of the impact of label noise correction on ML Fairness](https://mlsanity.com/paper/2306.15994/) (2023)
- [LaplaceConfidence: a Graph-based Approach for Learning with Noisy Labels](https://mlsanity.com/paper/2307.16614/) (2023)
- [Fair-OBNC: Correcting Label Noise for Fairer Datasets](https://mlsanity.com/paper/2410.06214/) (2024)

## Abstract (arXiv)

Fairness in machine learning (ML) has a critical importance for building trustworthy machine learning system as artificial intelligence (AI) systems increasingly impact various aspects of society, including healthcare decisions and legal judgments. Moreover, numerous studies demonstrate evidence of unfair outcomes in ML and the need for more robust fairness-aware methods. However, the data we use to train and develop debiasing techniques often contains biased and noisy labels. As a result, the label bias in the training data affects model performance and misrepresents the fairness of classifiers during testing. To tackle this problem, our paper presents Graph-based Fairness-aware Label Correction (GFLC), an efficient method for correcting label noise while preserving demographic parity in datasets. In particular, our approach combines three key components: prediction confidence measure, graph-based regularization through Ricci-flow-optimized graph Laplacians, and explicit demographic parity incentives. Our experimental findings show the effectiveness of our proposed approach and show significant improvements in the trade-off between performance and fairness metrics compared to the baseline.