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August 21, 2025

Balancing Fairness and Accuracy in the Development of Trustworthy AI Systems

Key Points

Our method improves fairness without compromising accuracy, emphasizing a balanced approach to AI.
Employing adaptive thresholds resulted in substantial bias reduction while maintaining or enhancing predictive accuracy.
The framework introduces unified metrics for assessing fairness, bias, and accuracy at multiple stages of model development.
These findings provide a blueprint for integrating fairness-accuracy considerations in AI production environments.

Abstract

Abstract Achieving a harmonious balance between fairness and predictive accuracy is critical for trustworthy AI. We present a component-level causal reasoning framework that identifies and mitigates bias early in the pipeline—during imputation, encoding, and sampling—thereby preserving overall model utility. Our approach introduces unified Statistical, Causal, and Counterfactual metrics that quantify disparities alongside accuracy and F1 scores at each stage. By employing adaptive thresholds, we ensure fairness improvements do not compromise accuracy. This targeted mitigation uncovers root causes tied to sensitive and correlated features, applies interventions precisely where needed, and prevents downstream distortions. Evaluated on five benchmark datasets (Adult Census Income, German Credit, COMPAS, Bank Marketing, Titanic), our method maintains or enhances accuracy while substantially reducing bias, demonstrating a practical path to balanced, equitable AI. These findings lay the groundwork for the BIASGUARD framework—a step-by-step blueprint for integrating fairness-accuracy auditing and adaptive mitigation in production pipelines.

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