Detecting and Mitigating Bias by Treating Fairness as a Symmetry Operation

Nishit Singh

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arXiv · 2606.06514

Detecting and Mitigating Bias by Treating Fairness as a Symmetry Operation

Nishit Singh

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Abstract

Machine learning systems deployed in high stakes socioeconomic settings routinely display bias. We formalize bias as a symmetry breaking operation: a classifier is fair if its outputs remain invariant under the counterfactual operation of switching a sensitive attribute, with merit features held fixed. We implement loss based regularization as a symmetry restoring mechanism and evaluate the framework on four synthetic datasets with varying levels of noise, correlation, and bias. The framework achieves upwards of 90\% violation reduction, with accuracy costs around 5\%. This framework does not require causal graph knowledge, is computationally lightweight, and generalizes to any sensitive attribute definable as a bit-flip, making it suitable for contexts where local sources of discrimination remain absent from mainstream benchmarks.

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Nishit Singh. 2026-06-02. Detecting and Mitigating Bias by Treating Fairness as a Symmetry Operation. https://arxiv.org/abs/2606.06514

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