Version 1
: Received: 8 June 2024 / Approved: 11 June 2024 / Online: 11 June 2024 (12:38:23 CEST)
Version 2
: Received: 11 June 2024 / Approved: 12 June 2024 / Online: 12 June 2024 (11:18:25 CEST)
How to cite:
Yuan, Y.; Chen, K.; Rizvi, M.; Baillie, L.; Pang, W. Quantifying the Cross-Sectoral Intersecting Discrepancies Within Multiple Groups Using Latent Class Analysis Towards Fairness. Preprints2024, 2024060700. https://doi.org/10.20944/preprints202406.0700.v2
Yuan, Y.; Chen, K.; Rizvi, M.; Baillie, L.; Pang, W. Quantifying the Cross-Sectoral Intersecting Discrepancies Within Multiple Groups Using Latent Class Analysis Towards Fairness. Preprints 2024, 2024060700. https://doi.org/10.20944/preprints202406.0700.v2
Yuan, Y.; Chen, K.; Rizvi, M.; Baillie, L.; Pang, W. Quantifying the Cross-Sectoral Intersecting Discrepancies Within Multiple Groups Using Latent Class Analysis Towards Fairness. Preprints2024, 2024060700. https://doi.org/10.20944/preprints202406.0700.v2
APA Style
Yuan, Y., Chen, K., Rizvi, M., Baillie, L., & Pang, W. (2024). Quantifying the Cross-Sectoral Intersecting Discrepancies Within Multiple Groups Using Latent Class Analysis Towards Fairness. Preprints. https://doi.org/10.20944/preprints202406.0700.v2
Chicago/Turabian Style
Yuan, Y., Lynne Baillie and Wei Pang. 2024 "Quantifying the Cross-Sectoral Intersecting Discrepancies Within Multiple Groups Using Latent Class Analysis Towards Fairness" Preprints. https://doi.org/10.20944/preprints202406.0700.v2
Abstract
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 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 between minority ethnic groups, highlighting the need for targeted interventions in real-world AI applications. Additionally, we demonstrate how the proposed approach can be used to provide insights into the fairness of machine learning.
Keywords
Machine Learning; AI for Social Science; AI bias
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
