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.
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
FairSplit decomposes the embedding space into a Fair Space for prediction and a Sensitive Space that isolates sensitive information, using a learnable mask and the Hilbert‑Schmidt Independence Criterion (HSIC) to guide the separation. The method simultaneously trains for classification accuracy and independence from sensitive attributes, providing theoretical bounds on the demographic parity gap based on the Fair Space dimensionality and HSIC. It supports multiclass tasks and non‑binary sensitive attributes, achieving competitive accuracy‑fairness trade‑offs on established benchmarks.
The paper introduces a node‑level relaxation of individual fairness called local predictive consistency, where each prediction is compared to a similarity‑weighted average of predictions in its local neighborhood. This approach yields an interpretable fairness score and a regularized learning method based on local residuals, and it explicitly constructs the neighborhood graph with threshold selection guided by graph‑coverage diagnostics. The authors apply the method to binary classification, regression, and time‑to‑event prediction, and compare it with pairwise constraint‑based fairness formulations to analyze fairness‑performance trade‑offs.
By Zhouting Zhao, Tin Lok James Ng
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
The paper introduces a post‑processing framework that uses model ensembling to achieve fairness‑aware predictions. It is designed to work independently of any specific model internals, making it applicable to a wide range of learning tasks, model architectures, and fairness definitions. Experiments on classification, regression, and survival analysis show that the framework improves fairness while preserving predictive accuracy.
By Zhouting Zhao, Tin Lok James Ng