Decomposing Direct and Indirect Biases in Linear Models under Demographic Parity Constraint

• URL: http://arxiv.org/abs/2511.11294v1
• Date: Fri, 14 Nov 2025 13:27:54 GMT
• Title: Decomposing Direct and Indirect Biases in Linear Models under Demographic Parity Constraint
• Authors: Bertille Tierny, Arthur Charpentier, François Hu,
• Abstract summary: We propose a post-processing framework to decompose the resulting bias into direct (sensitive-attribute) and indirect (correlated-features) components.<n>Our method analytically characterizes how demographic parity reshapes each model coefficient, including those of both sensitive and non-sensitive features.<n>Our framework requires no retraining and provides actionable insights for model auditing and mitigation.
• Score: 4.129225533930966
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Linear models are widely used in high-stakes decision-making due to their simplicity and interpretability. Yet when fairness constraints such as demographic parity are introduced, their effects on model coefficients, and thus on how predictive bias is distributed across features, remain opaque. Existing approaches on linear models often rely on strong and unrealistic assumptions, or overlook the explicit role of the sensitive attribute, limiting their practical utility for fairness assessment. We extend the work of (Chzhen and Schreuder, 2022) and (Fukuchi and Sakuma, 2023) by proposing a post-processing framework that can be applied on top of any linear model to decompose the resulting bias into direct (sensitive-attribute) and indirect (correlated-features) components. Our method analytically characterizes how demographic parity reshapes each model coefficient, including those of both sensitive and non-sensitive features. This enables a transparent, feature-level interpretation of fairness interventions and reveals how bias may persist or shift through correlated variables. Our framework requires no retraining and provides actionable insights for model auditing and mitigation. Experiments on both synthetic and real-world datasets demonstrate that our method captures fairness dynamics missed by prior work, offering a practical and interpretable tool for responsible deployment of linear models.

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