Factoring the matrix of domination: A critical review and reimagination of intersectionality in ai fairness

A Ovalle, A Subramonian, V Gautam, G Gee… - Proceedings of the …, 2023 - dl.acm.org
Intersectionality is a critical framework that, through inquiry and praxis, allows us to examine
how social inequalities persist through domains of structure and discipline. Given AI fairness' …

Learning fair representations via rebalancing graph structure

G Zhang, D Cheng, G Yuan, S Zhang - Information Processing & …, 2024 - Elsevier
Abstract Graph Neural Network (GNN) models have been extensively researched and
utilised for extracting valuable insights from graph data. The performance of fairness …

A survey on intersectional fairness in machine learning: Notions, mitigation, and challenges

U Gohar, L Cheng - arXiv preprint arXiv:2305.06969, 2023 - arxiv.org
The widespread adoption of Machine Learning systems, especially in more decision-critical
applications such as criminal sentencing and bank loans, has led to increased concerns …

Improving fairness in ai models on electronic health records: The case for federated learning methods

R Poulain, MF Bin Tarek, R Beheshti - … of the 2023 ACM conference on …, 2023 - dl.acm.org
Developing AI tools that preserve fairness is of critical importance, specifically in high-stakes
applications such as those in healthcare. However, health AI models' overall prediction …

Fairness-aware clique-preserving spectral clustering of temporal graphs

D Fu, D Zhou, R Maciejewski, A Croitoru… - Proceedings of the …, 2023 - dl.acm.org
With the widespread development of algorithmic fairness, there has been a surge of
research interest that aims to generalize the fairness notions from the attributed data to the …

Intersectional Two-sided Fairness in Recommendation

Y Wang, P Sun, W Ma, M Zhang, Y Zhang… - Proceedings of the …, 2024 - dl.acm.org
Fairness of recommender systems (RS) has attracted increasing attention recently. Based
on the involved stakeholders, the fairness of RS can be divided into user fairness, item …

Mining bias-target Alignment from Voronoi Cells

R Nahon, VT Nguyen… - Proceedings of the IEEE …, 2023 - openaccess.thecvf.com
Despite significant research efforts, deep neural networks remain vulnerable to biases: this
raises concerns about their fairness and limits their generalization. In this paper, we propose …

MultiFair: Model Fairness With Multiple Sensitive Attributes

H Tian, B Liu, T Zhu, W Zhou… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
While existing fairness interventions show promise in mitigating biased predictions, most
studies concentrate on single-attribute protections. Although a few methods consider …

Integrating Fair Representation Learning with Fairness Regularization for Intersectional Group Fairness

DQ Dzakpasu, J Liu, J Li, L Liu - Proceedings of the 33rd ACM …, 2024 - dl.acm.org
In the pursuit of intersectional group fairness in machine learning models, significant
attention has been directed towards fair representation learning methods. These methods …

Dealing with Data Bias in Classification: Can Generated Data Ensure Representation and Fairness?

MK Duong, S Conrad - International Conference on Big Data Analytics and …, 2023 - Springer
Fairness is a critical consideration in data analytics and knowledge discovery because
biased data can perpetuate inequalities through further pipelines. In this paper, we propose …