algorithmic fairness

Post-processing debiasing methods may inadvertently introduce new forms of unfairness—particularly through overcorrection caused by imbalanced prediction flips across demographic groups. To address this, we propose “Flip Disparity,” a novel metric suite that quantifies, for the first time in post-processing, the relative proportion of predictions flipped per group, thereby overcoming limitations of conventional fairness metrics that ignore transparency and proportionality in correction behavior. Our method leverages differences in confusion matrices and inter-group comparative analysis, integrated within a unified framework combining visual diagnostic tools and strategy-comparability assessment. This paradigm significantly enhances the interpretability of debiasing strategies and enables reliable detection of latent imbalanced corrections. Empirical evaluation across multiple benchmark datasets reveals previously undetected correction biases in widely adopted fairness algorithms. The proposed framework establishes a verifiable, auditable standard for responsible algorithmic governance.

This paper identifies a critical issue: when training and test data are drawn from the same source via random sampling, inherent data biases systematically distort fairness evaluation, leading to erroneous assessments for protected subgroups (e.g., gender, race). To address this, we propose FairMatch—the first fairness diagnostic method integrating Propensity Score Matching (PSM). FairMatch constructs comparable inter-subgroup sample pairs on the test set, dynamically optimizes subgroup-specific decision thresholds, and applies fairness-aware probability calibration to unmatched samples. By enabling precise bias localization and hierarchical mitigation, FairMatch preserves model predictive performance while significantly enhancing the reliability of fairness assessment and the effectiveness of bias mitigation.

This paper addresses the problem of biased predictions by machine learning models against marginalized groups in real-world data. To jointly optimize predictive accuracy and fairness, we propose a genetic algorithm-based sample weighting method that evolves instance-level weights through multi-objective optimization. Unlike conventional uniform or feature-driven weighting schemes, our approach simultaneously optimizes accuracy, AUC, demographic parity difference, and subgroup false negative rate. Extensive experiments on 11 publicly available datasets—including two healthcare benchmarks—demonstrate that the evolved weights substantially improve the fairness–performance trade-off. The most significant gains are achieved when jointly optimizing for accuracy and demographic parity difference, confirming the method’s effectiveness and generalizability in practical, high-stakes domains.

Machine learning models exhibit high sensitivity to minor perturbations in training data, leading to unstable predictions; yet conventional fairness metrics (e.g., bias-based indicators) ignore this prediction uncertainty. Method: We propose a variance-oriented paradigm for group fairness—introducing the first systematic framework that treats inter-group predictive variance equality as a core fairness criterion, grounded in statistical error decomposition and theoretical analysis of variance’s independent impact on fairness assessment. Contribution/Results: We release VarFair, the first open-source library integrating uncertainty quantification with fairness evaluation. Extensive experiments on Adult, COMPAS, and other benchmarks demonstrate that groups with high predictive variance are frequently misclassified as “fair” by standard methods, whereas our variance-aware metric significantly improves identification of disadvantaged groups and enhances assessment robustness under data perturbations.

This paper addresses the misalignment between algorithmic bias assessment and legal standards by proposing a quantification framework rigorously grounded in U.S. anti-discrimination law. Methodologically, it distinguishes legally salient discriminatory testing from systemic disparity through legal contextualization, and introduces the Objective Fairness Index (OFI)—a metric integrating objective test theory and measurement stability, using marginal benefit as a proxy to quantify legal compliance of algorithmic decisions. Its key contribution lies in being the first fairness metric to embed legal admissibility directly into its design, enabling a paradigm shift in algorithmic auditing from statistical fairness to legally grounded fairness. Empirical evaluation on real-world judicial prediction systems—including COMPAS—demonstrates that OFI reliably detects unlawful discrimination, offering regulators and auditors the first quantitative tool with both legal interpretability and operational utility.

This study investigates the compatibility conflict between statistical parity and equalized odds fairness criteria under scenarios characterized by base rate imbalance across sensitive groups or unreliable labeling. Through probabilistic modeling and theoretical analysis, it systematically demonstrates— for the first time—how disparities in base rates inherently preclude the simultaneous satisfaction of both fairness notions, and derives necessary and sufficient conditions for their incompatibility. The work further advocates for assessing base rate imbalance risks prior to adopting statistical parity, thereby offering a rigorous theoretical foundation and practical guidance for the design of fair algorithms, policy formulation, and compliance auditing.

This study addresses the inconsistency among fairness metrics in face recognition, where different measures often yield contradictory conclusions about model bias, thereby exposing the limitations of single-metric evaluation. To tackle this issue, the authors propose the Fairness Disagreement Index (FDI) to quantify the degree of disagreement across multiple fairness criteria and introduce a multidimensional evaluation framework that integrates both error rate disparities and performance-oriented fairness metrics. Through systematic experiments under controlled conditions, they demonstrate that such metric disagreement is pervasive across varying decision thresholds and model configurations, revealing a critical flaw in current fairness assessment practices. The work provides both a novel analytical tool and empirical evidence to support more comprehensive and reliable fairness evaluations in face recognition systems.

This study addresses how infra-marginality—differences in data distributions across groups—complicates judgments of AI fairness, as conventional statistical parity metrics often fail to align with human perceptions of fairness. Through a controlled user study involving 85 participants in a hypothetical medical decision-making scenario, the authors systematically investigate how group-specific model performance and training data availability shape fairness judgments. They find that when group-wise performance is equal or unknown, participants favor outcome equality; however, when performance disparities are attributable to data imbalance, models preserving these differences are perceived as more fair. These results demonstrate that human fairness judgments are not solely based on outcome equality but are significantly influenced by beliefs about the underlying causes of disparities, thereby challenging the prevailing assumption that statistical parity should serve as the default standard for algorithmic fairness.

To address the unclear sources of bias in linear models under demographic parity constraints, this paper proposes the first post-hoc fairness intervention framework that requires no model retraining. It explicitly decomposes bias into the direct effect of sensitive attributes and the indirect effects mediated through correlated features. Leveraging analytical derivation and statistical modeling, the method rigorously characterizes how fairness constraints reshape model coefficients and alter feature-level bias distributions. Unlike prior approaches, it imposes no strong distributional assumptions and retains explicit dependence on the sensitive attribute, thereby enhancing transparency and interpretability of fairness interventions. Experiments on synthetic and real-world datasets demonstrate that the framework captures dynamic fairness behaviors overlooked by existing methods, offering a practical, actionable tool for model auditing and bias attribution.

This study addresses the lack of systematic evaluation of robustness in existing fair machine learning methods under realistic data perturbations such as label noise, missing data, and distribution shifts. It introduces a causal inference framework to conduct the first comprehensive robustness analysis of mainstream fairness interventions—including sensitive attribute handling and bias mitigation techniques—under non-ideal data conditions. Empirical results demonstrate that several widely used approaches suffer significant performance degradation under common perturbations, thereby exposing critical limitations for real-world deployment. These findings provide both theoretical grounding and practical guidance for developing more reliable and robust fair machine learning systems.