Measuring Contextual Bias and Robustness in AI Systems: The ∆Q Fairness Metric

This study introduces the ∆Q (Delta Q) fairness metric, derived from contextual identity physics, as a novel approach to quantifying bias and robustness in artificial intelligence systems. Unlike traditional fairness measures based on linear differences, ∆Q captures the logarithmic strength of contextual transformations between demographic groups or operational settings. Applied to the Bias-in-Bios dataset with BERT-base and RoBERTa models, our experiments reveal significant gender bias (∆Q = -0.1306 ƀ and -0.1120 ƀ, respectively) that conventional metrics underestimate. By providing a universal, scale-invariant measure of contextual discrimination, the ∆Q framework offers practical applications in AI ethics, model robustness testing, and regulatory compliance. This work establishes ∆Q as a promising tool for bridging quantum-inspired contextual analysis with machine learning fairness.