Tootfinder

On the Consistency and Performance of the Iterative Bayesian Update
Ehab ElSalamouny, Catuscia Palamidessi
https://arxiv.org/abs/2508.09980 https://arxiv.o…

On the Consistency and Performance of the Iterative Bayesian Update
For many social, scientific, and commercial purposes, it is often important to estimate the distribution of the users' data regarding a sensitive attribute, e.g., their ages, locations, etc. To allow this estimation while protecting the users' privacy, every user applies a local privacy protection mechanism that releases a noisy (sanitized) version of their original datum to the data collector; then the original distribution is estimated using one of the known methods, such as the matrix invers…

Through Their Eyes: User Perceptions on Sensitive Attribute Inference of Social Media Videos by Visual Language Models
Shuning Zhang, Gengrui Zhang, Yibo Meng, Ziyi Zhang, Hantao Zhao, Xin Yi, Hewu Li
https://arxiv.org/abs/2508.07658

Through Their Eyes: User Perceptions on Sensitive Attribute Inference of Social Media Videos by Visual Language Models
The rapid advancement of Visual Language Models (VLMs) has enabled sophisticated analysis of visual content, leading to concerns about the inference of sensitive user attributes and subsequent privacy risks. While technical capabilities of VLMs are increasingly studied, users' understanding, perceptions, and reactions to these inferences remain less explored, especially concerning videos uploaded on the social media. This paper addresses this gap through a semi-structured interview (N=17), inve…

Exploring the Design Space of Fair Tree Learning Algorithms
Kiara Stempel, Mattia Cerrato, Stefan Kramer
https://arxiv.org/abs/2509.03204 https://arxiv.org…

Exploring the Design Space of Fair Tree Learning Algorithms
Decision trees have been studied extensively in the context of fairness, aiming to maximize prediction performance while ensuring non-discrimination against different groups. Techniques in this space usually focus on imposing constraints at training time, constraining the search space so that solutions which display unacceptable values of relevant metrics are not considered, discarded, or discouraged. If we assume one target variable y and one sensitive attribute s, the design space of tree lea…

Toward Robust Medical Fairness: Debiased Dual-Modal Alignment via Text-Guided Attribute-Disentangled Prompt Learning for Vision-Language Models
Yuexuan Xia, Benteng Ma, Jiang He, Zhiyong Wang, Qi Dou, Yong Xia
https://arxiv.org/abs/2508.18886

Toward Robust Medical Fairness: Debiased Dual-Modal Alignment via Text-Guided Attribute-Disentangled Prompt Learning for Vision-Language Models
Ensuring fairness across demographic groups in medical diagnosis is essential for equitable healthcare, particularly under distribution shifts caused by variations in imaging equipment and clinical practice. Vision-language models (VLMs) exhibit strong generalization, and text prompts encode identity attributes, enabling explicit identification and removal of sensitive directions. However, existing debiasing approaches typically address vision and text modalities independently, leaving residual…

Kernel-based Equalized Odds: A Quantification of Accuracy-Fairness Trade-off in Fair Representation Learning
Yijin Ni, Xiaoming Huo
https://arxiv.org/abs/2508.15084 https://

Kernel-based Equalized Odds: A Quantification of Accuracy-Fairness Trade-off in Fair Representation Learning
This paper introduces a novel kernel-based formulation of the Equalized Odds (EO) criterion, denoted as $EO_k$, for fair representation learning (FRL) in supervised settings. The central goal of FRL is to mitigate discrimination regarding a sensitive attribute $S$ while preserving prediction accuracy for the target variable $Y$. Our proposed criterion enables a rigorous and interpretable quantification of three core fairness objectives: independence (prediction $\hat{Y}$ is independent of $S$),…

Information-Theoretic Fairness with A Bounded Statistical Parity Constraint
Amirreza Zamani, Abolfazl Changizi, Ragnar Thobaben, Mikael Skoglund
https://arxiv.org/abs/2508.12847

Information-Theoretic Fairness with A Bounded Statistical Parity Constraint
In this paper, we study an information-theoretic problem of designing a fair representation that attains bounded statistical (demographic) parity. More specifically, an agent uses some useful data $X$ to solve a task $T$. Since both $X$ and $T$ are correlated with some sensitive attribute or secret $S$, the agent designs a representation $Y$ that satisfies a bounded statistical parity and/or privacy leakage constraint, that is, such that $I(Y;S) \leq ε$. Here, we relax the perfect demographic …