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Algorithmic fairness

Found by the topic model: algorithmic fairness.

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7papers this week−36.4% vs last week39 this month

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Papers

  • arXiv cs.LGNov 11

    Robustness Within a Fairness Budget: Parity Frontiers and Equal-Opportunity Reversal

    Pablo Rivas, Jeremy Frank

    Adversarial training protects predictive performance under attack but does not determine how decisions are distributed across protected groups. We formulate binary classification as projected gradient descent (PGD) adversarial-risk minimization under a squared soft demographic-parity (DP) budget, optimized with projected multiplier and one-sided penalty updates. On Adult Income, using the binary sex field supplied by the benchmark for group auditing but excluding it from prediction, we test logistic regression and a one-hidden-layer multilayer perceptron (MLP) across three paired seeds, four PGD strengths, and five budgets. At $\varepsilon=0.3$, PGD training improves balanced accuracy over empirical-risk minimization (ERM) by $7.77$ and $11.45$ percentage points and removes $85.7\%$ and $89.5\%$ of attack-induced growth in hard demographic-parity difference (DPD). At the representative interior value $τ=0.015$, constrained training cuts DPD relative to PGD training by $52.2\%$ and $49.2\%$, while remaining $3.18$ and $8.34$ balanced-accuracy points above ERM. Across all eight seed-averaged model-attack paths, the sampled budget path strictly orders DPD and accuracy and reverses…

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  • arXiv cs.LGNov 11

    Characterizing Predictive Multiplicity in Ranking and Its Fairness Implications

    Ainhize Barrainkua, Mikel Malagón

    Prediction problems often admit competing models that achieve comparable performance yet produce conflicting outputs, a phenomenon known as predictive multiplicity. While this challenge has been extensively studied in classification tasks, its implications remain largely unexplored in ranking frameworks, which are critical for high-stakes societal decisions such as the allocation of welfare resources and social assistance. This paper investigates the phenomenon of predictive multiplicity in score-based ranking problems. We introduce formal definitions to capture multiplicity in ranking settings, supported by foundational theoretical guarantees. Furthermore, we examine the dual relationship between predictive multiplicity and algorithmic fairness in ranking problems: we analyze how model multiplicity can act as a potential source of unfairness, while demonstrating how exploring this decision space provides a principled avenue for discovering less discriminatory rankings. Finally, we complement our theoretical contributions with insightful empirical evaluations on both synthetic and real-world datasets, validating our analytical findings and illustrating their practical…

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  • arXiv cs.LGNov 11

    Overcoming the Resolution Limit: Significance-Aware Regularization for Intersectional Fairness

    Antonio Ferrara, André Panisson, Francesco Cozzi, Alan Perotti, Francesco Bonchi

    Intersectional fairness requires evaluating models across fine-grained subgroups. However, as group definitions become more granular, the sample size per subgroup shrinks, causing fairness estimates to become increasingly unstable. Existing in-processing methods are certainty-blind: they penalize observed disparities without distinguishing statistically certified unfairness from sampling noise. This leads to fairness overfitting, where models sacrifice utility to "fix" spurious disparities in sparse regimes. To address this, we introduce the significant-excess metric family, a continuous, uncertainty-aware fairness score that measures only the portion of a subgroup's disparity exceeding what sampling variation can explain at a specific confidence level. Building on this metric, we propose SAFER (Significance-Aware Fairness Regularizer), a differentiable in-processing method that gates fairness penalties using a soft-count likelihood-ratio evidence statistic. SAFER satisfies a provable safety property: when every subgroup disparity lies well below the gate threshold, the unconstrained optimum is nearly stationary for SAFER. At that optimum, the back-propagated contribution of its…

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  • arXiv stat.MLNov 11

    Synthetic Data Augmentation for Fairness under Data Imbalance: Theory and Adaptive Allocation

    Zhengchi Ma, Kaifeng Zhang, Anru R. Zhang

    Data imbalance across sensitive groups and class labels is common in classification problems and can contribute to substantial fairness disparities. We study how such imbalance affects Equal Opportunity and how synthetic data augmentation can mitigate the resulting unfairness. We first identify two mechanisms, prevalence imbalance and group-size imbalance, through which changes in the training distribution shift the learned decision boundary and induce disparities in group true positive rates. Motivated by this analysis, we study exact full-balance augmentation, which equalizes all group-label cells. We establish conditions under which full balancing strictly improves Equal Opportunity together with a prediction-error comparison under the oracle balanced distribution, and show that the Equal Opportunity gap of the synthetic-data classifier converges to that of the oracle balanced classifier as the sample size grows and the generator error vanishes. The finite-sample convergence rate agrees, up to logarithmic factors and generator-approximation error, with the information-theoretic lower-bound rate under the corresponding regularity regimes. Since exact balancing need not provide…

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  • arXiv stat.MLNov 10

    Robbing Peter to Pay Paul: Cross-Attribute Externalities of Fairness Interventions

    Fernandes Machado Agathe, Jamotton Charlotte, Charpentier Arthur

    Fairness interventions often targets disparities associated with a single sensitive attribute in binary classification tasks. However, when multiple sensitive attributes are associated with a data subject, improving fairness with respect to one attribute does not necessarily improve it across all attributes: it may exacerbate disparities associated with other sensitive attributes, including those that are unavailable to the practitioner. We refer to these unintended effects as cross-attribute externalities of fairness. We study how fairness with respect to one sensitive attribute varies when an initially unfair classification or regression model is corrected with respect to another attribute. We characterize these externalities using Wasserstein barycentric disparities and develop a distributional framework that captures changes in both marginal and joint fairness measures. This highlights a limitation of conventional fairness evaluation, in which the success of an intervention is assessed mainly for the attribute it targets. In a jointly Gaussian model, we derive explicit trajectories for marginal and joint disparities and identify conditions under which an intervention…

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  • arXiv stat.MLNov 9

    Hidden Tails: Certifying Tail-Risk Claims under Selective Labels

    El Mustapha Mansouri

    Historical decisions determine which outcomes are observed, potentially concealing a predictor's largest errors. Even unlimited data from the same observation process can therefore leave population tail risk unresolved. Here we derive sharp bounds on conditional value-at-risk, the average loss in a specified upper tail, for binary outcomes and bounded losses. These bounds span exactly the risks compatible with the observations and a stated limit on how outcome odds differ between observed and hidden cases at the same covariate values. Under stated regularity conditions, calibrated statistical certificates cover these ranges simultaneously across candidate predictors, supporting risk limits and model comparisons while separating statistical uncertainty from persistent ambiguity. For a fixed predictor and loss threshold, we derive a minimum-cost allocation for reducing excess-loss ambiguity by prioritizing hidden outcomes according to ambiguity removed per unit cost. This allocation treats audited population contributions as known and retains the original sensitivity bounds separately for audited and still-hidden outcomes. In controlled masking experiments with known loan…

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  • arXiv cs.LGNov 9

    Explanation-Aware Fairness Beyond Output Parity: Joint Output-Attribution Regularization for Medical Image Classification

    Gökhan Özbulak, Oscar Jimenez-del-Toro, Lilian Berton, André Anjos

    Output-level fairness does not constrain whether demographic groups rely on similar predictive contributions in representation space. For a nonlinear encoder with a linear head, we show that outcome-conditioned output score disparity equals the signed sum of feature attribution differences across subgroups by allowing small prediction gaps to arise through cancellation effect. We test whether an attribution disparity penalty complements output-level equalized odds based fairness surrogates. Across 960 DINOv2-LoRA models on HAM10000 and Harvard-GF datasets for gender and race fairness, attribution augmentation increases utility-fairness trade-off performance for hypervolume in six different settings with paired 95% confidence intervals and relative gains of 1.49-10.57%. Under a predefined utility constraint, augmented model families also achieve 9.0-38.7% lower equalized odds difference, lower attribution disparity, and reduced cancellation ratio while preserving predictive utility. These results support attribution fairness as a complementary explanation-aware constraint on output fairness.

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