The paper introduces a new framework for evaluating spatial fairness in predictive models by considering individuals’ activity spaces rather than just a single residential location. It proposes associating users with multiple geographic partitions and applying a spatial scan statistic to detect unfairness across these activity-space patterns. Experiments on synthetic datasets demonstrate the method’s effectiveness in identifying unfair treatment and retrieving affected objects, while highlighting a trade‑off in localization performance across resolutions.
arXiv:2605. 23234v3 Announce Type: replace Abstract: Assessing the spatial fairness of predictive models involves establishing whether they are statistically penalizing (favoring) individuals associated with certain geographical locations.
arXiv:2608.24818v1 Announce Type: new Abstract: Neighborhood-based fairness audits evaluate individual fairness by comparing predictions among similar individuals in feature space. Despite their wide...
arXiv:2604. 16610v2 Announce Type: replace-cross Abstract: Machine learning models often inherit biases from historical data, raising critical concerns about fairness and accountability.
The paper introduces REMI, a framework that treats counterfactual fairness as a relational invariant discovery problem. By learning over paired examples, REMI identifies input regions where fairness is violated and generates interpretable rule-based models—fairness invariants—that can block or relabel unfair predictions without retraining the underlying model. Experiments on symbolic and neural network programs show REMI localizes fairness bugs in over 83% of cases and reduces discriminatory decisions in black-box models by up to 70%.
arXiv:2602. 18201v2 Announce Type: replace Abstract: Unsupervised representations are widely assumed to be neutral with respect to sensitive attributes when those attributes are withheld from training.
arXiv:2607. 05101v1 Announce Type: new Abstract: The application of machine learning-based predictive algorithms to Anti-Money Laundering (AML) has grown rapidly, driven by the vast volume of financial transaction data available to banks.
arXiv:2606. 26200v1 Announce Type: cross Abstract: Modern machine learning systems have outgrown their origins as isolated predictive constructs, evolving into complex socio-technical architectures that actively mediate human opportunity.
arXiv:2609.07959v1 Announce Type: cross Abstract: Machine learning models are widely used in clinical applications, social media, law enforcement and critical infrastructure. Verifying whether their...
arXiv:2407. 14766v4 Announce Type: replace-cross Abstract: This paper presents a philosophical and experimental study of fairness interventions in AI classification, centered on the explainability and transparency of corrective methods, and on the opposition between two fairness criteria, namely Demographic Parity and Equalized Odds.
arXiv:2606. 20461v1 Announce Type: new Abstract: Machine learning models have been shown to exhibit discriminatory outcomes or degraded performance for individuals at the intersection of multiple sensitive attributes, such as race and gender.
arXiv:2304. 13917v4 Announce Type: replace Abstract: In recent years, there has been a surge in effort to formalize notions of fairness in machine learning.
arXiv:2606. 00656v1 Announce Type: cross Abstract: Ensuring fair and equitable treatment across diverse groups, particularly in multi-class classification tasks, poses a significant challenge due to the persistent biases inherent in machine learning models.