Detecting and Mitigating Bias by Treating Fairness as a Symmetry Operation
This paper formalizes bias as a symmetry breaking operation and uses loss-based regularization to restore symmetry, achieving over 90% violation reduction with only about 5% accuracy cost on synthetic datasets. The framework requires no causal graph knowledge, is computationally light, and generalizes to any bit-flip definable sensitive attribute.
[2606.06514] Detecting and Mitigating Bias by Treating Fairness as a Symmetry Operation
[Submitted on 2 Jun 2026]
Title:Detecting and Mitigating Bias by Treating Fairness as a Symmetry Operation
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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.
Comments: 8 pages, 7 figures
Subjects:
Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as: arXiv:2606.06514 [cs.AI]
(or arXiv:2606.06514v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.06514
arXiv-issued DOI via DataCite
Submission history
From: Nishit Singh [view email] [v1] Tue, 2 Jun 2026 09:42:54 UTC (1,832 KB)
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