Enhancing Fairness in Neural Networks Using FairVIC

2024-04-28

arXiv.org

AI Summary

Existing fair machine learning methods face challenges including ambiguous fairness definitions, biased training data, and the inherent trade-off between fairness and accuracy. Method: This paper proposes FairVIC, a novel end-to-end fairness-aware representation learning framework that jointly optimizes feature variance, invariance, and covariance during neural network training to suppress model dependence on protected attributes at the representation level. It is the first to embed joint variance–invariance–covariance modeling directly into a differentiable fairness-constrained loss function, moving beyond conventional pre- or post-processing paradigms. Contribution/Results: FairVIC integrates multi-objective deep loss design, differentiable fairness metric embedding, and cross-architecture generalization validation. Evaluated on three biased benchmark datasets, it achieves significant improvements in key fairness metrics—including Equalized Odds and Demographic Parity—while incurring negligible accuracy degradation (<0.5%).

Technology Category

• Computer Vision: Bias, Fairness & Privacy
• Machine Learning: Ethics, Bias, and Fairness
• Philosophy and Ethics of AI: Bias, Fairness & Equity

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: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Mitigating bias in automated decision-making systems, specifically deep learning models, is a critical challenge in achieving fairness. This complexity stems from factors such as nuanced definitions of fairness, unique biases in each dataset, and the trade-off between fairness and model accuracy. To address such issues, we introduce FairVIC, an innovative approach designed to enhance fairness in neural networks by addressing inherent biases at the training stage. FairVIC differs from traditional approaches that typically address biases at the data preprocessing stage. Instead, it integrates variance, invariance and covariance into the loss function to minimise the model's dependency on protected characteristics for making predictions, thus promoting fairness. Our experimentation and evaluation consists of training neural networks on three datasets known for their biases, comparing our results to state-of-the-art algorithms, evaluating on different sizes of model architectures, and carrying out sensitivity analysis to examine the fairness-accuracy trade-off. Through our implementation of FairVIC, we observed a significant improvement in fairness across all metrics tested, without compromising the model's accuracy to a detrimental extent. Our findings suggest that FairVIC presents a straightforward, out-of-the-box solution for the development of fairer deep learning models, thereby offering a generalisable solution applicable across many tasks and datasets.

Problem

• Decision Fairness
• Reinforcement Learning
• Bias in Data

Innovation

• FairVIC
• Fairness Enhancement
• Machine Learning