# Quantifying the Cross-sectoral Intersecting Discrepancies within Multiple Groups Using Latent Class Analysis Towards Fairness

arXiv: https://arxiv.org/abs/2407.03133 · HTML: https://mlsanity.com/paper/2407.03133/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 41 references that are in the map.
- First posted on arXiv 24 May 2024.

## Builds on (most cited first, 8 of 8)

- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning](https://mlsanity.com/paper/1609.06570/) (2016)
- [Fairness And Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, And Mitigation Strategies](https://mlsanity.com/paper/2304.07683/) (2023)
- [Does "AI" stand for augmenting inequality in the era of covid-19 healthcare?](https://mlsanity.com/paper/2105.07844/) (2021)
- [Fairness Assessment for Artificial Intelligence in Financial Industry](https://mlsanity.com/paper/1912.07211/) (2019)
- [Experts' View on Challenges and Needs for Fairness in Artificial Intelligence for Education](https://mlsanity.com/paper/2207.01490/) (2022)
- [StepMix: A Python Package for Pseudo-Likelihood Estimation of Generalized Mixture Models with External Variables](https://mlsanity.com/paper/2304.03853/) (2023)
- [Quantifying Health Inequalities Induced by Data and AI Models](https://mlsanity.com/paper/2205.01066/) (2022)

## Abstract (arXiv)

The growing interest in fair AI development is evident. The ''Leave No One Behind'' initiative urges us to address multiple and intersecting forms of inequality in accessing services, resources, and opportunities, emphasising the significance of fairness in AI. This is particularly relevant as an increasing number of AI tools are applied to decision-making processes, such as resource allocation and service scheme development, across various sectors such as health, energy, and housing. Therefore, exploring joint inequalities in these sectors is significant and valuable for thoroughly understanding overall inequality and unfairness. This research introduces an innovative approach to quantify cross-sectoral intersecting discrepancies among user-defined groups using latent class analysis. These discrepancies can be used to approximate inequality and provide valuable insights to fairness issues. We validate our approach using both proprietary and public datasets, including both EVENS and Census 2021 (England & Wales) datasets, to examine cross-sectoral intersecting discrepancies among different ethnic groups. We also verify the reliability of the quantified discrepancy by conducting a correlation analysis with a government public metric. Our findings reveal significant discrepancies both among minority ethnic groups and between minority ethnic groups and non-minority ethnic groups, emphasising the need for targeted interventions in policy-making processes. Furthermore, we demonstrate how the proposed approach can provide valuable insights into ensuring fairness in machine learning systems.