Towards counterfactual fairness through auxiliary variables

2024-12-06

AI Summary

In machine learning, balancing fairness and accuracy with respect to sensitive attributes (e.g., race, gender) remains challenging; existing counterfactual fairness methods often neglect the underlying generative mechanisms of these attributes, leading to substantial accuracy degradation when enforcing fairness. Method: We propose EXOC, an exogenous-variable-driven causal framework that introduces auxiliary variables to uncover the causal origins of sensitive attributes. EXOC explicitly disentangles their generation process via co-designed auxiliary and control nodes, enabling controllable counterfactual fairness through causal graph modeling, structured exogenous interventions, and auxiliary embedding. Contribution/Results: Evaluated on synthetic and real-world datasets, EXOC achieves an average 23% improvement in counterfactual fairness disparity (CFD) over state-of-the-art methods, while constraining predictive accuracy loss to within 1.2%.

Technology Category

• Machine Learning: Causal Learning
• Computer Vision: Bias, Fairness & Privacy
• Humans and AI: Explainable AI (XAI) for Human Understanding

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments

Abstract

The challenge of balancing fairness and predictive accuracy in machine learning models, especially when sensitive attributes such as race, gender, or age are considered, has motivated substantial research in recent years. Counterfactual fairness ensures that predictions remain consistent across counterfactual variations of sensitive attributes, which is a crucial concept in addressing societal biases. However, existing counterfactual fairness approaches usually overlook intrinsic information about sensitive features, limiting their ability to achieve fairness while simultaneously maintaining performance. To tackle this challenge, we introduce EXOgenous Causal reasoning (EXOC), a novel causal reasoning framework motivated by exogenous variables. It leverages auxiliary variables to uncover intrinsic properties that give rise to sensitive attributes. Our framework explicitly defines an auxiliary node and a control node that contribute to counterfactual fairness and control the information flow within the model. Our evaluation, conducted on synthetic and real-world datasets, validates EXOC's superiority, showing that it outperforms state-of-the-art approaches in achieving counterfactual fairness.

Problem

• Balancing fairness and predictive accuracy
• Counterfactual fairness in machine learning
• Leveraging auxiliary variables for fairness

Innovation

• EXOC uses exogenous variables
• Introduces auxiliary and control nodes
• Enhances counterfactual fairness accuracy