REMI, a framework for the automated localization, explanation, and mitigation of individual discrimination, is presented, which introduces a bidirectional relational explanation framework that learns over paired examples $(x, x')$ to identify regions of the input space where fairness is violated.
Abstract
Data-driven software systems are increasingly deployed in high-stakes socio-economic domains, from criminal justice to financial lending. However, these systems often exhibit individual discrimination---unjustified disparities in which a program yields different outcomes for similar individuals who differ only in their protected attributes (e.g., race, gender, age). While existing research has focused on detecting and quantifying these bugs, there remains a critical lack of principled mechanisms to explain and localize individual fairness bugs. Current explanation techniques are largely designed for single-input decisions rather than the relational nature of discrimination, which inherently involves a comparison between an original and a counterfactual pair. We present REMI, a framework for the automated localization, explanation, and mitigation of individual discrimination. Inspired by loop-invariant synthesis in formal methods, we treat counterfactual fairness as a relational invariant discovery problem. We introduce a bidirectional relational explanation framework that learns over paired examples $(x, x')$ to identify regions of the input space where fairness is violated. Unlike traditional one-way implication pairs used in invariant inference, our approach enforces bidirectional constraints: requiring identical outcomes for both original and counterfactual samples. REMI utilizes three data-alignment techniques to infer interpretable rule-based models that act as"fairness invariants."These rules serve as guardrails to selectively block or relabel unfair predictions without requiring model retraining. Our evaluation on symbolic and neural network programs demonstrates that REMI localizes ground-truth fairness bugs in over 83% of cases, significantly outperforming state-of-the-art baselines and reducing discriminatory decisions in black-box models by up to 70%.
A dominant critique of algorithmic fairness holds that increasing fairness reduces predictive accuracy, imposing a cost on society. We challenge that assumption by empirically analyzing the COMPAS dataset. We make two contributions. First, using causal inference methods, we show that racial bias is not only present in the COMPAS dataset but is also amplified by the models trained on it. Widely used models do more than replicate existing bias; they exacerbate it. This undercuts both the assumption that algorithmic decision-making offers a neutral improvement over human judgment and the weaker claim that it merely mirrors preexisting human bias. Second, we reframe the fairness-accuracy tradeoff. Applying fairness constraints does not necessarily cost predictive accuracy in criminal justice. Prediction systems operationalize concepts such as risk through implicit and often flawed normative choices about what to predict and how. The tradeoff claim assumes that the unconstrained model's prediction is an optimal baseline. Fairness constraints can instead correct distortions introduced by biased outcome variables: rearrest data, in this case, captures and magnifies systemic racial disparities. Under some interventions, therefore, fairness carries none of the cost presumed in policy debates. These dynamics extend beyond criminal justice to lending, hiring, and housing, where biased outcome variables reinforce inequality independently of proxy selection. We draw out what this implies for how law and policy should approach fairness adjustments in criminal law.
Ignacio Cofone, Warut Khern-am-nuai University of Oxford, McGill University· 3 citations
This position paper argues that fairness failures in generative models, albeit driven by multiple factors, are ultimately stemming from an evaluation problem: fairness findings are rarely comparable across papers or actionable for deployment decisions.
M. Vladimirova, Jean-Yves Franceschi, Thibaut Issenhuth· 0 citations
All fairness algorithms unavoidably rely on normative assumptions about fair treatment, yet these assumptions often remain implicit. We argue that these assumptions should be formalized as explicit, auditable policies and introduce FAIR-SHEPHERD, a structural policy-based framework for transparent fairness in real-world settings with noisy labels and shifting distributions. FAIR-SHEPHERD uses gradients as attribution signals, encoded in a Structural Fairness Specification (SFS) that defines vertical coherence and orthogonality to vulnerable proxies. We introduce SFS metrics: Vertical Coherence Score (VCS) to measure directional coherence across adjacent normative slices, and Horizontal Leakage Score (HLS), augmented by a signed directional variant, to detect gradient alignment with policy-declared vulnerable or proxy directions. To enforce these policies, we develop S-agnostic tools that optimize policy-aligned gradient objectives using lattice-defined proxies. We demonstrate that outcome-based auditing is brittle to measurement error: under 10% label noise on Adult, Worst-Group AUC for ERM drops by 0.159. In contrast, our gradient-based structural metrics provide a label-agnostic audit of the decision logic, remaining stable even when evaluation labels are corrupted. Specialized fairness baselines including ARL, JTT, and GoG retain substantial structural leakage on COMPAS, with HLS values from about 0.47 to 0.71. Some also reduce EOD relative to ERM, which shows that outcome and structural criteria can diverge. Using our S-agnostic Gradient Penalty tools, we reduce policy-specified structural leakage by roughly 85% on COMPAS (0.478 → 0.073) and over 94% on Adult (0.138 → 0.008) while retaining competitive AUC and Worst-Group AUC. We have released our source code to facilitate further research here.
Shubham Mohole, Hongjun Choi, Christian Schroeder et al.· ACM Journal on Responsible C...· 0 citations
Graph Counterfactual Fairness (GCFair), a novel framework that achieves counterfactual fairness by explicitly identifying and disentangling the subsets of node features and graph structures genuinely affected by sensitive attributes, is proposed.
Zichong Wang, Zhipeng Yin, Zhong Chen et al.· 2 citations
Fairness in automated scoring is typically evaluated with a single global statistic contrasting a focal and reference group-an approach that can either mask a disparity that changes sign across the ability range, or overstate one by conflating it with genuine ability differences between groups (impact). We introduce condfair, an R package that adapts differential item functioning (DIF) logic to automated scoring: it estimates a conditional disparity function across ability levels, tests it with a wild-bootstrap omnibus procedure, decomposes bias into uniform and non-uniform components, and identifies candidate feature-level sources of a detected disparity via conditional SHAP disparity testing. Using the PERSUADE 2.0 essay corpus, we show a global measure can conceal a large, ability-concentrated gender disparity (marginal gap = 0.003; peak conditional disparity = 0.276, p = .001) while overstating an English Language Learner disparity by conflating it with impact (marginal gap = 0.280; conditional bias = 0.045).
Tri Zahra Ningsih, Aman Aman, Ahmad Nasrulloh· Applied Psychological Measur...· 0 citations