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.
By Francesco Lettich, Mario A. Nascimento, Chiara Pugliese, Chiara Renso
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...
By Binita Maity
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%.
By Ranit Debnath Akash, Ashish Kumar, Gang Tan, Saeid Tizpaz-Niari
arXiv:2604. 16610v2 Announce Type: replace-cross Abstract: Machine learning models often inherit biases from historical data, raising critical concerns about fairness and accountability.
By Yixiao Lin, James Booth
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.
By Lea Multerer, Michele Inchingolo, David Kletz, Adrian Cosma, Alessandro Antonucci, Martina Gogova
The paper "Fair Like Us? Auditing LLM Alignment in Resource Allocation" presents a method for evaluating how large language models reason about fairness in the allocation of scarce, indivisible resources. It compares LLMs’ first‑person fairness judgments with human responses across various scenarios, finding that models tend to favor stricter fairness constraints, exhibit more self‑interested behavior, and are sensitive to framing. The study also shows that current fine‑tuning datasets struggle to align LLM judgments with human ones.
By Qishen Han, Hadi Hosseini, Joshua Kavner, Samarth Khanna, Sujoy Sikdar, Lirong Xia