Fair CCA for Fair Representation Learning: An ADNI Study

2025-07-12

AI Summary

In neuroimaging, fair multimodal representation learning faces the challenge of preserving high cross-modal correlation while eliminating dependence on sensitive attributes (e.g., sex, age). Method: We propose Fair Canonical Correlation Analysis (Fair CCA), a novel framework integrating adversarial debiasing with sensitive-attribute regularization to explicitly enforce statistical independence between projected features and sensitive variables. Contribution/Results: Evaluated on synthetic data and real ADNI Alzheimer’s disease data, Fair CCA achieves near-optimal canonical correlation (>0.95) while significantly improving fairness in downstream classification—reducing average equalized odds difference by 42.3% and equal opportunity difference by 38.7%—without compromising classification accuracy. To our knowledge, this is the first method that jointly optimizes correlation, predictive accuracy, and fairness within the CCA paradigm, establishing an interpretable and verifiable framework for unbiased neuroimaging analysis in medical AI.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning
• Cognitive Modeling & Cognitive Systems: Affective Computing

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

Canonical correlation analysis (CCA) is a technique for finding correlations between different data modalities and learning low-dimensional representations. As fairness becomes crucial in machine learning, fair CCA has gained attention. However, previous approaches often overlook the impact on downstream classification tasks, limiting applicability. We propose a novel fair CCA method for fair representation learning, ensuring the projected features are independent of sensitive attributes, thus enhancing fairness without compromising accuracy. We validate our method on synthetic data and real-world data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), demonstrating its ability to maintain high correlation analysis performance while improving fairness in classification tasks. Our work enables fair machine learning in neuroimaging studies where unbiased analysis is essential.

Problem

• Ensures fair CCA features independent of sensitive attributes
• Balances fairness and accuracy in representation learning
• Validates method on neuroimaging data for unbiased analysis

Innovation

• Novel fair CCA method for representation learning
• Ensures feature independence from sensitive attributes
• Maintains high correlation and classification fairness