AGENTIC-SYSTEMSJune 8, 20261 min read11 views

Detecting and Mitigating Bias by Treating Fairness as a Symmetry Operation

Researchers propose a novel framework that treats fairness in machine learning as a symmetry operation, mitigating bias by over 90% with minimal impact on accuracy.

Computer Science > Artificial Intelligence

Title:Detecting and Mitigating Bias by Treating Fairness as a Symmetry Operation

View PDF HTML (experimental)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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Source: arXiv cs.AI Recent

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