Tootfinder

GAMBIT : A Challenge Set for Evaluating Gender Bias in Machine Translation Quality Estimation Metrics
Giorgos Filandrianos, Orfeas Menis Mastromichalakis, Wafaa Mohammed, Giuseppe Attanasio, Chrysoula Zerva
https://arxiv.org/abs/2510.06841

GAMBIT+: A Challenge Set for Evaluating Gender Bias in Machine Translation Quality Estimation Metrics
Gender bias in machine translation (MT) systems has been extensively documented, but bias in automatic quality estimation (QE) metrics remains comparatively underexplored. Existing studies suggest that QE metrics can also exhibit gender bias, yet most analyses are limited by small datasets, narrow occupational coverage, and restricted language variety. To address this gap, we introduce a large-scale challenge set specifically designed to probe the behavior of QE metrics when evaluating translat…

Bias and Coverage Properties of the WENDy-IRLS Algorithm
Abhi Chawla, David M. Bortz, Vanja Dukic
https://arxiv.org/abs/2510.03365 https://arxiv.org/pdf/25…

Bias and Coverage Properties of the WENDy-IRLS Algorithm
The Weak form Estimation of Nonlinear Dynamics (WENDy) method is a recently proposed class of parameter estimation algorithms that exhibits notable noise robustness and computational efficiency. This work examines the coverage and bias properties of the original WENDy-IRLS algorithm's parameter and state estimators in the context of the following differential equations: Logistic, Lotka-Volterra, FitzHugh-Nagumo, Hindmarsh-Rose, and a Protein Transduction Benchmark. The estimators' performance w…

Media Coverage of War Victims: Journalistic Biases in Reporting on Israel and Gaza
Bedoor AlShebli, Bruno Gabriel Salvador Casara, Anne Maass
https://arxiv.org/abs/2510.06453 ht…

Media Coverage of War Victims: Journalistic Biases in Reporting on Israel and Gaza
October 7th 2023 marked the start of a war against Gaza, which is considered one of the most devastating wars in modern history and has led to a stark attitudinal divide within and between countries. To investigate the role of media bias in reporting on this asymmetrical warfare, we analyzed over 14,000 news articles published during the first year of war in three Western (The New York Times, BBC, CNN) and one non-Western English-language outlets (Al Jazeera English). Exploring the media narrat…

Ask Me Again Differently: GRAS for Measuring Bias in Vision Language Models on Gender, Race, Age, and Skin Tone
Shaivi Malik, Hasnat Md Abdullah, Sriparna Saha, Amit Sheth
https://arxiv.org/abs/2508.18989

Ask Me Again Differently: GRAS for Measuring Bias in Vision Language Models on Gender, Race, Age, and Skin Tone
As Vision Language Models (VLMs) become integral to real-world applications, understanding their demographic biases is critical. We introduce GRAS, a benchmark for uncovering demographic biases in VLMs across gender, race, age, and skin tone, offering the most diverse coverage to date. We further propose the GRAS Bias Score, an interpretable metric for quantifying bias. We benchmark five state-of-the-art VLMs and reveal concerning bias levels, with the least biased model attaining a GRAS Bias S…

Exploring the cosmic microwave background dipole direction using gamma-ray bursts
Orlando Luongo, Marco Muccino, Francesco Sorrenti
https://arxiv.org/abs/2508.04304 https://

Exploring the cosmic microwave background dipole direction using gamma-ray bursts
We search for dipole variations in the Hubble constant $H_0$ using gamma-ray burst (GRB) data, as such anisotropies may shed light on the Hubble tension. We employ the most recent and reliable GRB catalogs from the $E_{p}-E_{iso}$ and the $L_0-E_{p}-T$ correlations. Despite their large uncertainties, GRBs are particularly suited for this analysis due to their redshift coverage up to $z\sim9$, their isotropic sky distribution that minimizes directional bias, and their strong correlations whose n…

Evaluating the performance of Bayesian cumulative logistic models in randomised controlled trials: a simulation study
Chris J. Selman, Katherine J. Lee, Steven Y. C. Tong, Mark Jones, Robert K. Mahar
https://arxiv.org/abs/2507.21473

Evaluating the performance of Bayesian cumulative logistic models in randomised controlled trials: a simulation study
Background: The proportional odds (PO) model is the most common analytic method for ordinal outcomes in randomised controlled trials. While parameter estimates obtained under departures from PO can be interpreted as an average odds ratio, they can obscure differing treatment effects across the distribution of the ordinal categories. Extensions to the PO model exist and this work evaluates their performance under deviations to the PO assumption. Methods: We evaluated the bias, coverage and mea…