FairTTTS: A Tree Test Time Simulation Method for Fairness-Aware Classification

2025-01-14

AI Summary

To address algorithmic bias in machine learning classification models against disadvantaged groups, this paper proposes a test-time fairness correction method for tree-based models that requires no retraining. The method introduces Tree-based Test-Time Simulation (TTTS)—the first application of TTTS to fairness optimization—and designs a distance-aware protected-attribute node decision adjustment mechanism to jointly improve both accuracy and fairness. It supports multiple fairness metrics and is compatible with post-processing bias mitigation and path reweighting. Experiments across seven benchmark datasets demonstrate an average fairness improvement of 20.96%, significantly outperforming baseline and state-of-the-art methods. Notably, prediction accuracy increases by 0.55%, whereas competing approaches incur an average accuracy drop of 0.42%.

Technology Category

• Machine Learning: Ethics, Bias, and Fairness
• Computer Vision: Bias, Fairness & Privacy
• Search and Optimization: Sampling/Simulation-based Search

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

Algorithmic decision-making has become deeply ingrained in many domains, yet biases in machine learning models can still produce discriminatory outcomes, often harming unprivileged groups. Achieving fair classification is inherently challenging, requiring a careful balance between predictive performance and ethical considerations. We present FairTTTS, a novel post-processing bias mitigation method inspired by the Tree Test Time Simulation (TTTS) method. Originally developed to enhance accuracy and robustness against adversarial inputs through probabilistic decision-path adjustments, TTTS serves as the foundation for FairTTTS. By building on this accuracy-enhancing technique, FairTTTS mitigates bias and improves predictive performance. FairTTTS uses a distance-based heuristic to adjust decisions at protected attribute nodes, ensuring fairness for unprivileged samples. This fairness-oriented adjustment occurs as a post-processing step, allowing FairTTTS to be applied to pre-trained models, diverse datasets, and various fairness metrics without retraining. Extensive evaluation on seven benchmark datasets shows that FairTTTS outperforms traditional methods in fairness improvement, achieving a 20.96% average increase over the baseline compared to 18.78% for related work, and further enhances accuracy by 0.55%. In contrast, competing methods typically reduce accuracy by 0.42%. These results confirm that FairTTTS effectively promotes more equitable decision-making while simultaneously improving predictive performance.

Problem

• Machine Learning
• Fairness
• Classification Bias

Innovation

• FairTTTS
• BiasReduction
• PredictionAccuracyEnhancement