Tootfinder

Justice Ketanji Brown Jackson is "breaking the fourth wall, speaking beyond the court,”
said Melissa Murray, a law professor at New York University.
💥“She is alarmed at what the court is doing and is sounding that in a different register,
one that is less concerned with the appearance of collegiality and more concerned with how the court appears to the public.”
Her slashing critiques sometimes seemed to test her colleagues’ patience,
culminating in an unchar…

Justice Ketanji Brown Jackson Makes Herself Heard, Prompting a Rebuke
In solo dissents this term, the justice accused the conservative majority of lawless bias. On the term’s last day, Justice Amy Coney Barrett fired back.

CSST Cosmological Emulator III: Hybrid Lagrangian Bias Expansion Emulation of Galaxy Clustering
Shuren Zhou, Zhao Chen, Yu Yu
https://arxiv.org/abs/2506.04671

CSST Cosmological Emulator III: Hybrid Lagrangian Bias Expansion Emulation of Galaxy Clustering
Galaxy clustering is an important probe in the upcoming China Space Station Telescope (CSST) survey to understand the structure growth and reveal the nature of the dark sector. However, it is a long-term challenge to model this biased tracer and connect the observable to the underlying physics. In this work, we present a hybrid Lagrangian bias expansion emulator, combining the Lagrangian bias expansion and the accurate dynamical evolution from $N$-body simulation, to predict the power spectrum …

A Framework for Auditing Chatbots for Dialect-Based Quality-of-Service Harms
Emma Harvey, Rene F. Kizilcec, Allison Koenecke
https://arxiv.org/abs/2506.04419

A Framework for Auditing Chatbots for Dialect-Based Quality-of-Service Harms
Increasingly, individuals who engage in online activities are expected to interact with large language model (LLM)-based chatbots. Prior work has shown that LLMs can display dialect bias, which occurs when they produce harmful responses when prompted with text written in minoritized dialects. However, whether and how this bias propagates to systems built on top of LLMs, such as chatbots, is still unclear. We conduct a review of existing approaches for auditing LLMs for dialect bias and show tha…

Just finished watching Mandalorian.
How would you describe it to your friend to convince them it’s worth watching?
(I will admit that I like storylines that have an end; even if I might not like the end. I completely dislike unfinished stories. It’s my be a bias, as I am under the impression that in the recent years TV series avoid completing their story arcs.)

This https://arxiv.org/abs/2505.10661 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

It's only fair when I think it's fair: How Gender Bias Alignment Undermines Distributive Fairness in Human-AI Collaboration
Human-AI collaboration is increasingly relevant in consequential areas where AI recommendations support human discretion. However, human-AI teams' effectiveness, capability, and fairness highly depend on human perceptions of AI. Positive fairness perceptions have been shown to foster trust and acceptance of AI recommendations. Yet, work on confirmation bias highlights that humans selectively adhere to AI recommendations that align with their expectations and beliefs -- despite not being necessa…

Exploring Gender Bias Beyond Occupational Titles
Ahmed Sabir, Rajesh Sharama
https://arxiv.org/abs/2507.02679 https://arxiv.org/pdf/2…

Exploring Gender Bias Beyond Occupational Titles
In this work, we investigate the correlation between gender and contextual biases, focusing on elements such as action verbs, object nouns, and particularly on occupations. We introduce a novel dataset, GenderLexicon, and a framework that can estimate contextual bias and its related gender bias. Our model can interpret the bias with a score and thus improve the explainability of gender bias. Also, our findings confirm the existence of gender biases beyond occupational stereotypes. To validate o…

A Comprehensive Survey on Bio-Inspired Algorithms: Taxonomy, Applications, and Future Directions
Shriyank Somvanshi, Md Monzurul Islam, Syed Aaqib Javed, Gaurab Chhetri, Kazi Sifatul Islam, Tausif Islam Chowdhury, Sazzad Bin Bashar Polock, Anandi Dutta, Subasish Das
https://arxiv.org/abs/2506.04238…

A Comprehensive Survey on Bio-Inspired Algorithms: Taxonomy, Applications, and Future Directions
Bio-inspired algorithms (BIAs) utilize natural processes such as evolution, swarm behavior, foraging, and plant growth to solve complex, nonlinear, high-dimensional optimization problems. This survey categorizes BIAs into eight groups: evolutionary, swarm intelligence, physics-inspired, ecosystem and plant-based, predator-prey, neural-inspired, human-inspired, and hybrid approaches, and reviews their core principles, strengths, and limitations. We illustrate the usage of these algorithms in mac…

This https://arxiv.org/abs/2503.09969 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Detecting Dataset Bias in Medical AI: A Generalized and Modality-Agnostic Auditing Framework
Artificial Intelligence (AI) is now firmly at the center of evidence-based medicine. Despite many success stories that edge the path of AI's rise in healthcare, there are comparably many reports of significant shortcomings and unexpected behavior of AI in deployment. A major reason for these limitations is AI's reliance on association-based learning, where non-representative machine learning datasets can amplify latent bias during training and/or hide it during testing. To unlock new tools capa…

The Bias of Subspace-based Data-Driven Predictive Control
Keith Moffat, Florian D\"orfler, Alessandro Chiuso
https://arxiv.org/abs/2507.02468 https://…

The Bias of Subspace-based Data-Driven Predictive Control
This paper quantifies and addresses the bias of subspace-based Data-Driven Predictive Control (DDPC) for linear, time-invariant (LTI) systems. The primary focus is the bias that arises when the training data is gathered with a feedback controller in closed-loop with the system. First, the closed-loop bias of Subspace Predictive Control is quantified using the training data innovations. Next, the bias of direct, subspace-based DDPC methods DeePC and $γ$-DDPC is shown to consist of two parts--th…

An interview with Gaia Marcus, director of the UK-based think tank Ada Lovelace Institute, on AI regulation in the UK and Europe, AI safety, bias, and more (Melissa Heikkilä/Financial Times)

Ada Lovelace Institute’s Gaia Marcus: regulation would increase people’s comfort with AI
The head of the UK-based think-tank talks about her hopes and fears for future oversight of AI

This https://arxiv.org/abs/2311.14590 has been replaced.
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Stability of domain walls with inflationary fluctuations under potential bias, and gravitational wave signatures
A recent study has shown that domain walls with inflationary initial fluctuations exhibit remarkable stability against population bias due to long-range correlations, challenging the claims of prior research. In this paper, we study the dynamics of these domain walls in the presence of potential bias and show that they collapse with a lifetime several times longer than that due to thermal fluctuations. This is interpreted as a difference in the average distance between domain walls, leading us …

This https://arxiv.org/abs/2410.06415 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

Biased AI can Influence Political Decision-Making
As modern large language models (LLMs) become integral to everyday tasks, concerns about their inherent biases and their potential impact on human decision-making have emerged. While bias in models are well-documented, less is known about how these biases influence human decisions. This paper presents two interactive experiments investigating the effects of partisan bias in LLMs on political opinions and decision-making. Participants interacted freely with either a biased liberal, biased conser…

Meta’s banning of two anti-Zionist comedians from Instagram is the latest example of Big Tech’s deep anti-Palestinian bias
#meta #facebook #instagram

Law360 now requires every story go through an AI-powered "bias" detection tool before publication after a LexisNexis exec accused the newsroom of liberal bias (Andrew Deck/Nieman Lab)
https://www.niemanlab.org/2025/07/law360-m

#KINutzen #Retröt
Können KI-Modelle für medizinische #Röntgenbilder unbewusst diskriminieren? Eine neue Studie zeigt, dass selbst auf Expertenniveau arbeitende Systeme wie

Bias in der Medizin: Wenn KI-Röntgen-Diagnostik nicht neutral ist
Wie fair ist KI in der Medizin wirklich? Diese Studie offenbart Verzerrungen in der Diagnose marginalisierter Gruppen.

This https://arxiv.org/abs/2501.07849 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

The Invisible Hand: Unveiling Provider Bias in Large Language Models for Code Generation
Large Language Models (LLMs) have emerged as the new recommendation engines, surpassing traditional methods in both capability and scope, particularly in code generation. In this paper, we reveal a novel provider bias in LLMs: without explicit directives, these models show systematic preferences for services from specific providers in their recommendations (e.g., favoring Google Cloud over Microsoft Azure). To systematically investigate this bias, we develop an automated pipeline to construct t…

X-ray Irradiation Studies on the Monopix DMAPS in 150$\,$nm and 180$\,$nm
Christian Bespin, Marlon Barbero, Pierre Barrillon, Patrick Breugnon, Ivan Caicedo, Yavuz Degerli, Jochen Dingfelder, Tomasz Hemperek, Toko Hirono, Hans Kr\"uger, Fabian H\"ugging, Konstantinos Moustakas, Patrick Pangaud, Heinz Pernegger, Petra Riedler, Piotr Rymaszewski, Lars Schall, Philippe Schwemling, Walter Snoeys, Tianyang Wang, Norbert Wermes, Sinou Zhang

X-ray Irradiation Studies on the Monopix DMAPS in 150$\,$nm and 180$\,$nm
Monolithic active pixel sensors with depleted substrates present a promising option for pixel detectors in high-radiation environments. High-resistivity silicon substrates and high bias voltage capabilities in commercial CMOS technologies facilitate depletion of the charge sensitive volume. TJ-Monopix2 and LF-Monopix2 are the most recent large-scale chips in their respective development line, aiming for the ATLAS Inner Tracker outer layer requirements. Those include a tolerance to ionizing radi…

🇺🇦 #NowPlaying on #BBC6Music's #6Musics00sForever
Zed Bias:
🎵 Neighbourhood
#ZedBias
https://tonyquattro.bandcamp.com/album/zed-bias-neighbourhood-tony-quattros-saturday-night-mix
https://open.spotify.com/track/6J6QyLMIoToFr1VvDpt8jo

This https://arxiv.org/abs/2412.04338 has been replaced.
initial toot: https://mastoxiv.page/@ar…

Numerical Aspects of Large Deviations
An introduction to numerical large-deviation sampling is provided. First, direct biasing with a known distribution is explained. As simple example, the Bernoulli experiment is used throughout the text. Next, Markov chain Monte Carlo (MCMC) simulations are introduced. In particular, the Metropolis-Hastings algorithm is explained. As first implementation of MCMC, sampling of the plain Bernoulli model is shown. Next, an exponential bias is used for the same model, which allows one to obtain the ta…

This https://arxiv.org/abs/2410.23818 has been replaced.
initial toot: https://mastoxiv.page/@…

Acceleration-induced transport of quantum vortices in joined atomtronic circuits
Persistent currents--inviscid quantized flow around an atomic circuit--are a crucial building block of atomtronic devices. We investigate how acceleration influences the transfer of persistent currents between two density-connected, ring-shaped atomic Bose-Einstein condensates, joined by a tunable weak link that controls system topology. We find that the acceleration of this system modifies both the density and phase dynamics between the rings, leading to a bias in the periodic vortex oscillati…

If it was up to me, I'd ONLY save the ugly animals https://www.abc.net.au/news/2025-06-25/ugly-endangered-animals-ignored-in-conservation-bias/105443062

Wildlife experts say ugly endangered animals are being ignored in conservation efforts
While there are plenty of conservation efforts and community love for cute marsupials like the western ringtail possum, researchers say that pretty privilege is threatening the existence of blood suckers and web weavers.

I just ran across this on LinkedIn: yet another study showing that LLMs embed and amplify human biases in the training data—this time in resume screening.
Yet this is the technology Trump, Musk, and DOGE want to use to rewrite things like HUD regulations and to replace human support agents.

Gender, race, and intersectional bias in AI resume screening via language model retrieval
Kyra Wilson and Aylin Caliskan examine gender, race, and intersectional bias in AI resume screening and suggest protective policies.

Scatter in the star formation rate-halo mass relation: secondary bias and its impact on line-intensity mapping
Rui Lan Jun (UTokyo), Tom Theuns (ICC, Durham), Kana Moriwaki (UTokyo), Sownak Bose (ICC, Durham)
https://arxiv.org/abs/2506.03015

Scatter in the star formation rate-halo mass relation: secondary bias and its impact on line-intensity mapping
We use the IllustrisTNG cosmological hydrodynamical simulations to study the impact of secondary bias -- specifically, the correlation between star formation rate (SFR) and halo bias at fixed halo mass -- on the line-intensity mapping (LIM) power spectrum. In LIM, the galaxy contributions are flux-weighted, and for many emission lines (e.g., H$α$), flux scales with SFR. We show that the (ensemble-averaged) large-scale two-halo term of the power spectrum depends only on the mean luminosity-halo…

This https://arxiv.org/abs/2505.21091 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCY_…

Position is Power: System Prompts as a Mechanism of Bias in Large Language Models (LLMs)
System prompts in Large Language Models (LLMs) are predefined directives that guide model behaviour, taking precedence over user inputs in text processing and generation. LLM deployers increasingly use them to ensure consistent responses across contexts. While model providers set a foundation of system prompts, deployers and third-party developers can append additional prompts without visibility into others' additions, while this layered implementation remains entirely hidden from end-users. As…

This https://arxiv.org/abs/2506.03074 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

GL-LowPopArt: A Nearly Instance-Wise Minimax Estimator for Generalized Low-Rank Trace Regression
We present `GL-LowPopArt`, a novel Catoni-style estimator for generalized low-rank trace regression. Building on `LowPopArt` (Jang et al., 2024), it employs a two-stage approach: nuclear norm regularization followed by matrix Catoni estimation. We establish state-of-the-art estimation error bounds, surpassing existing guarantees (Fan et al., 2019; Kang et al., 2022), and reveal a novel experimental design objective, $\mathrm{GL}(π)$. The key technical challenge is controlling bias from the non…

Excellent keynote in the #SemDH2025 workshop by Laura Hollink on Cultural Bias in Linked Open Data. Laura is addressing all bias related aspects in cultural heritage items itself, in the data representing it, the data schemata, vocabularies, and ontologies on which the data are based, as well as in the knowledge representation languages used to create the schemata.

EMO-Debias: Benchmarking Gender Debiasing Techniques in Multi-Label Speech Emotion Recognition
Yi-Cheng Lin, Huang-Cheng Chou, Yu-Hsuan Li Liang, Hung-yi Lee
https://arxiv.org/abs/2506.04652

EMO-Debias: Benchmarking Gender Debiasing Techniques in Multi-Label Speech Emotion Recognition
Speech emotion recognition (SER) systems often exhibit gender bias. However, the effectiveness and robustness of existing debiasing methods in such multi-label scenarios remain underexplored. To address this gap, we present EMO-Debias, a large-scale comparison of 13 debiasing methods applied to multi-label SER. Our study encompasses techniques from pre-processing, regularization, adversarial learning, biased learners, and distributionally robust optimization. Experiments conducted on acted and …

Crowd-SFT: Crowdsourcing for LLM Alignment
Alex Sotiropoulos, Sulyab Thottungal Valapu, Linus Lei, Jared Coleman, Bhaskar Krishnamachari
https://arxiv.org/abs/2506.04063

Crowd-SFT: Crowdsourcing for LLM Alignment
Large Language Models (LLMs) increasingly rely on Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF) to align model responses with human preferences. While RLHF employs a reinforcement learning approach with a separate reward model, SFT uses human-curated datasets for supervised learning. Both approaches traditionally depend on small, vetted groups of annotators, making them costly, prone to bias, and limited in scalability. We propose an open, crowd-sourced fine…

Is Reasoning All You Need? Probing Bias in the Age of Reasoning Language Models
Riccardo Cantini, Nicola Gabriele, Alessio Orsino, Domenico Talia
https://arxiv.org/abs/2507.02799 …

Is Reasoning All You Need? Probing Bias in the Age of Reasoning Language Models
Reasoning Language Models (RLMs) have gained traction for their ability to perform complex, multi-step reasoning tasks through mechanisms such as Chain-of-Thought (CoT) prompting or fine-tuned reasoning traces. While these capabilities promise improved reliability, their impact on robustness to social biases remains unclear. In this work, we leverage the CLEAR-Bias benchmark, originally designed for Large Language Models (LLMs), to investigate the adversarial robustness of RLMs to bias elicitat…

When LLMs Disagree: Diagnosing Relevance Filtering Bias and Retrieval Divergence in SDG Search
William A. Ingram, Bipasha Banerjee, Edward A. Fox
https://arxiv.org/abs/2507.02139 …

When LLMs Disagree: Diagnosing Relevance Filtering Bias and Retrieval Divergence in SDG Search
Large language models (LLMs) are increasingly used to assign document relevance labels in information retrieval pipelines, especially in domains lacking human-labeled data. However, different models often disagree on borderline cases, raising concerns about how such disagreement affects downstream retrieval. This study examines labeling disagreement between two open-weight LLMs, LLaMA and Qwen, on a corpus of scholarly abstracts related to Sustainable Development Goals (SDGs) 1, 3, and 7. We is…

Structure and Smoothness Constrained Dual Networks for MR Bias Field Correction
Dong Liang, Xingyu Qiu, Yuzhen Li, Wei Wang, Kuanquan Wang, Suyu Dong, Gongning Luo
https://arxiv.org/abs/2507.01326

Structure and Smoothness Constrained Dual Networks for MR Bias Field Correction
MR imaging techniques are of great benefit to disease diagnosis. However, due to the limitation of MR devices, significant intensity inhomogeneity often exists in imaging results, which impedes both qualitative and quantitative medical analysis. Recently, several unsupervised deep learning-based models have been proposed for MR image improvement. However, these models merely concentrate on global appearance learning, and neglect constraints from image structures and smoothness of bias field, le…

Spectral Bias in Variational Quantum Machine Learning
Callum Duffy, Marcin Jastrzebski
https://arxiv.org/abs/2506.22555 https://arxiv…

Spectral Bias in Variational Quantum Machine Learning
In this work, we investigate the phenomenon of spectral bias in quantum machine learning, where, in classical settings, models tend to fit low-frequency components of a target function earlier during training than high-frequency ones, demonstrating a frequency-dependent rate of convergence. We study this effect specifically in parameterised quantum circuits (PQCs). Leveraging the established formulation of PQCs as Fourier series, we prove that spectral bias in this setting arises from the ``red…

This https://arxiv.org/abs/2506.01832 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCC_…

Pseudorandom bits for non-commutative programs
We obtain new explicit pseudorandom generators for several computational models involving groups. Our main results are as follows: 1. We consider read-once group-products over a finite group $G$, i.e., tests of the form $\prod_{i=1}^n g_i^{x_i}$ where $g_i\in G$, a special case of read-once permutation branching programs. We give generators with optimal seed length $c_G \log(n/\varepsilon)$ over any $p$-group. The proof uses the small-bias plus noise paradigm, but derandomizes the noise to av…

This https://arxiv.org/abs/2412.03824 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

Towards Data Governance of Frontier AI Models
Data is essential to train and fine-tune today's frontier artificial intelligence (AI) models and to develop future ones. To date, academic, legal, and regulatory work has primarily addressed how data can directly harm consumers and creators, such as through privacy breaches, copyright infringements, and bias and discrimination. Our work, instead, focuses on the comparatively neglected question of how data can enable new governance capacities for frontier AI models. This approach for "frontier …

Recover Experimental Data with Selection Bias using Counterfactual Logic
Jingyang He, Shuai Wang, Ang Li
https://arxiv.org/abs/2506.00335 https://

Recover Experimental Data with Selection Bias using Counterfactual Logic
Selection bias, arising from the systematic inclusion or exclusion of certain samples, poses a significant challenge to the validity of causal inference. While Bareinboim et al. introduced methods for recovering unbiased observational and interventional distributions from biased data using partial external information, the complexity of the backdoor adjustment and the method's strong reliance on observational data limit its applicability in many practical settings. In this paper, we formally di…

Visualization for interactively adjusting the de-bias effect of word embedding
Arisa Sugino, Takayuki Itoh
https://arxiv.org/abs/2506.02447 https://…

Visualization for interactively adjusting the de-bias effect of word embedding
Word embedding, which converts words into numerical values, is an important natural language processing technique and widely used. One of the serious problems of word embedding is that the bias will be learned and affect the model if the dataset used for pre-training contains bias. On the other hand, indiscriminate removal of bias from word embeddings may result in the loss of information, even if the bias is undesirable to us. As a result, a risk of model performance degradation due to bias re…

What Does Information Science Offer for Data Science Research?: A Review of Data and Information Ethics Literature
Brady D. Lund, Ting Wang
https://arxiv.org/abs/2506.03165

What Does Information Science Offer for Data Science Research?: A Review of Data and Information Ethics Literature
This paper reviews literature pertaining to the development of data science as a discipline, current issues with data bias and ethics, and the role that the discipline of information science may play in addressing these concerns. Information science research and researchers have much to offer for data science, owing to their background as transdisciplinary scholars who apply human-centered and social-behavioral perspectives to issues within natural science disciplines. Information science resea…

UniSite: The First Cross-Structure Dataset and Learning Framework for End-to-End Ligand Binding Site Detection
Jigang Fan, Quanlin Wu, Shengjie Luo, Liwei Wang
https://arxiv.org/abs/2506.03237

UniSite: The First Cross-Structure Dataset and Learning Framework for End-to-End Ligand Binding Site Detection
The detection of ligand binding sites for proteins is a fundamental step in Structure-Based Drug Design. Despite notable advances in recent years, existing methods, datasets, and evaluation metrics are confronted with several key challenges: (1) current datasets and methods are centered on individual protein-ligand complexes and neglect that diverse binding sites may exist across multiple complexes of the same protein, introducing significant statistical bias; (2) ligand binding site detection …

Rodrigues Network for Learning Robot Actions
Jialiang Zhang, Haoran Geng, Yang You, Congyue Deng, Pieter Abbeel, Jitendra Malik, Leonidas Guibas
https://arxiv.org/abs/2506.02618

Rodrigues Network for Learning Robot Actions
Understanding and predicting articulated actions is important in robot learning. However, common architectures such as MLPs and Transformers lack inductive biases that reflect the underlying kinematic structure of articulated systems. To this end, we propose the Neural Rodrigues Operator, a learnable generalization of the classical forward kinematics operation, designed to inject kinematics-aware inductive bias into neural computation. Building on this operator, we design the Rodrigues Network …

The Triangle Friendship Paradox
Bishakh Bhattacharya, Nitya Gadhiwala, Frank den Hollander, Pradeeptha R Jain, Tejashree Subramanya
https://arxiv.org/abs/2507.02627

The Triangle Friendship Paradox
We consider the generalised friendship paradox, focussing on the number of triangles at a vertex as the relevant attribute. We show that, contrary to the setting where the attribute is the number of edges at a vertex or the number of wedges at a vertex, the average friendship-bias of the number of triangles at a vertex is not always non-negative. We identify classes of finite deterministic graphs for which the bias is non-negative, and provide examples of finite deterministic graphs for which i…

Multilevel Stochastic Gradient Descent for Optimal Control Under Uncertainty
Niklas Baumgarten, David Schneiderhan
https://arxiv.org/abs/2506.02647 https:/…

Multilevel Stochastic Gradient Descent for Optimal Control Under Uncertainty
We present a multilevel stochastic gradient descent method for the optimal control of systems governed by partial differential equations under uncertain input data. The gradient descent method used to find the optimal control leverages a parallel multilevel Monte Carlo method as stochastic gradient estimator. As a result, we achieve precise control over the stochastic gradient's bias, introduced by numerical approximation, and its sampling error, arising from the use of incomplete gradients, wh…

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control
Zilin Kang, Chenyuan Hu, Yu Luo, Zhecheng Yuan, Ruijie Zheng, Huazhe Xu
https://arxiv.org/abs/2507.02712

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control
Deep reinforcement learning for continuous control has recently achieved impressive progress. However, existing methods often suffer from primacy bias, a tendency to overfit early experiences stored in the replay buffer, which limits an RL agent's sample efficiency and generalizability. In contrast, humans are less susceptible to such bias, partly due to infantile amnesia, where the formation of new neurons disrupts early memory traces, leading to the forgetting of initial experiences. Inspired…

Breaking the Barriers of Text-Hungry and Audio-Deficient AI
Hamidou Tembine, Issa Bamia, Massa NDong, Bakary Coulibaly, Oumar Issiaka Traore, Moussa Traore, Moussa Sanogo, Mamadou Eric Sangare, Salif Kante, Daryl Noupa Yongueng, Hafiz Tiomoko Ali, Malik Tiomoko, Frejus Laleye, Boualem Djehiche, Wesmanegda Elisee Dipama, Idris Baba Saje, Hammid Mohammed Ibrahim, Moumini Sanogo, Marie Coursel Nininahazwe, Abdul-Latif Siita, Haine Mhlongo, Teddy Nelvy Dieu Merci Kouka, Mariam Serine Jerid…

Breaking the Barriers of Text-Hungry and Audio-Deficient AI
While global linguistic diversity spans more than 7164 recognized languages, the current dominant architecture of machine intelligence remains fundamentally biased toward written text. This bias excludes over 700 million people particularly in rural and remote regions who are audio-literate. In this work, we introduce a fully textless, audio-to-audio machine intelligence framework designed to serve this underserved population, and all the people who prefer audio-efficiency. Our contributions in…

This https://arxiv.org/abs/2409.17800 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

Bias Assessment and Data Drift Detection in Medical Image Analysis: A Survey
Machine Learning (ML) models have gained popularity in medical imaging analysis given their expert level performance in many medical domains. To enhance the trustworthiness, acceptance, and regulatory compliance of medical imaging models and to facilitate their integration into clinical settings, we review and categorise methods for ensuring ML reliability, both during development and throughout the model's lifespan. Specifically, we provide an overview of methods assessing models' inner-workin…

Stereotype Detection as a Catalyst for Enhanced Bias Detection: A Multi-Task Learning Approach
Aditya Tomar, Rudra Murthy, Pushpak Bhattacharyya
https://arxiv.org/abs/2507.01715

Stereotype Detection as a Catalyst for Enhanced Bias Detection: A Multi-Task Learning Approach
Bias and stereotypes in language models can cause harm, especially in sensitive areas like content moderation and decision-making. This paper addresses bias and stereotype detection by exploring how jointly learning these tasks enhances model performance. We introduce StereoBias, a unique dataset labeled for bias and stereotype detection across five categories: religion, gender, socio-economic status, race, profession, and others, enabling a deeper study of their relationship. Our experiments c…

I just ran across this on LinkedIn: yet another study showing that LLMs embed and amplify human biases in the training data—this time in resume screening.
Yet this is the technology Trump, Musk, and DOGE want to use to rewrite things like HUD regulations and to replace human support agents.

Gender, race, and intersectional bias in AI resume screening via language model retrieval
Kyra Wilson and Aylin Caliskan examine gender, race, and intersectional bias in AI resume screening and suggest protective policies.

Differentiable Fuzzy Cosmic-Web for Field Level Inference
P. Rossell\'o, F. -S. Kitaura, D. Forero-S\'anchez, F. Sinigaglia, G. Favole
https://arxiv.org/abs/2506.03969

Differentiable Fuzzy Cosmic-Web for Field Level Inference
A comprehensive analysis of the cosmological large-scale structure derived from galaxy surveys involves field-level inference, which requires a forward modelling framework that simultaneously accounts for structure formation and tracer bias. While structure formation models are well-understood, the development of an effective field-level bias model remains imprecise, particularly in the context of tracer perturbation theory within Bayesian reconstruction methods, which we address in this work. …

This https://arxiv.org/abs/2305.11943 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSI_…

Public versus Less-Public News Engagement on Facebook: Patterns Across Bias and Reliability
The rapid growth of social media as a news platform has raised significant concerns about the influence and societal impact of biased and unreliable news on these platforms. While much research has explored user engagement with news on platforms like Facebook, most studies have focused on publicly shared posts. This focus leaves an important question unanswered: how representative is the public sphere of Facebook's entire ecosystem? Specifically, how much of the interactions occur in less-publi…

This https://arxiv.org/abs/2504.03393 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Effects of Interpolation Error and Bias on the Random Mesh Finite Element Method for Inverse Problems
Bayesian inverse problems are an important application for probabilistic solvers of partial differential equations: when fully resolving numerical error is computationally infeasible, probabilistic solvers can be used to consistently model the error and propagate it to the posterior. In this work, the performance of the random mesh finite element method (RM-FEM) is investigated in a Bayesian inverse setting. We show how interpolation error negatively affects the RM-FEM posterior, and how these …

Triboelectric charge transfer theory driven by interfacial thermoelectric effect
Ji-Ho Mun, Eui-Cheol Shin, Jaeuk Seo, Yong-Hyun Kim
https://arxiv.org/abs/2507.02528

Triboelectric charge transfer theory driven by interfacial thermoelectric effect
Despite extensive study and the practical significance of friction-driven static electricity, a quantitative triboelectric charge transfer theory has yet to be established. Here, we elucidate the detailed dynamics of triboelectric charge transfer driven by interfacial thermoelectric bias maintaining a steady state at the interface. We demonstrate that triboelectric charge exists in a delta-like distribution at a steady state. We suggest that the transferred triboelectric charge is dictated by h…

Sehr viel KI auf der #republica.
Ein Thema, die mich interessiert, das ich aber im Programm nicht gefunden habe:
Wenn wir verweigern, dass KI mit unserem 'guten' Content 'gefüttert' wird, wird der Bias dann nicht noch stärker?
Gibt's dazu Forschung?

A systematic bias in template-based RV extraction algorithms
Andr\'e M. Silva, N. C. Santos, J. P. Faria, J. H. C. Martins, E. A. S. Cristo, S. G. Sousa, P. T. P. Viana, \'E. Artigau, K. Al Moulla, A. Castro-Gonz\'alez, D. F. M. Folha, P. Figueira, T. Schmidt, F. Pepe, X. Dumusque, O. D. S. Demangeon, T. L. Campante, X. Delfosse, B. Wehbe, J. Lillo-Box, A. R. Costa Silva, J. Rodrigues, J. I. Gonz\'alez Hern\'andez, T. Azevedo Silva, S. Cristiani, H. M. Tabernero, E.…

A systematic bias in template-based RV extraction algorithms
In this paper we identify and explore a previously unidentified, multi meter-per-second, systematic correlation between time and RVs inferred through TM and LBL methods. We evaluate the influence of the data-driven stellar template in the RV bias and hypothesize on the possible sources of this effect. We first use the s-BART pipeline to extract RVs from three different datasets gathered over four nights of ESPRESSO and HARPS observations. Then, we demonstrate that the effect can be recovered on…

Glitches far from transient gravitational-wave events do not bias inference
Sophie Hourihane, Katerina Chatziioannou
https://arxiv.org/abs/2506.21869 https…

Glitches far from transient gravitational-wave events do not bias inference
Non-Gaussian noise in gravitational-wave detectors, known as "glitches," can bias the inferred parameters of transient signals when they occur nearby in time and frequency. These biases are addressed with a variety of methods that remove or otherwise mitigate the impact of the glitch. Given the computational cost and human effort required for glitch mitigation, we study the conditions under which it is strictly necessary. We consider simulated glitches and gravitational-wave signals in various …

FAIR-MATCH: A Multi-Objective Framework for Bias Mitigation in Reciprocal Dating Recommendations
Madhav Kotecha
https://arxiv.org/abs/2507.01063 https://…

FAIR-MATCH: A Multi-Objective Framework for Bias Mitigation in Reciprocal Dating Recommendations
Online dating platforms have fundamentally transformed the formation of romantic relationships, with millions of users worldwide relying on algorithmic matching systems to find compatible partners. However, current recommendation systems in dating applications suffer from significant algorithmic deficiencies, including but not limited to popularity bias, filter bubble effects, and inadequate reciprocity modeling that limit effectiveness and introduce harmful biases. This research integrates fou…

Bias-field-free operation of nitrogen-vacancy ensembles in diamond for accurate vector magnetometry
Lilian Childress, Vincent Halde, Kayla Johnson, Andrew Lowther, David Roy-Guay, Romain Ruhlmann, Adrian Solyom
https://arxiv.org/abs/2505.24574

Bias-field-free operation of nitrogen-vacancy ensembles in diamond for accurate vector magnetometry
Accurate measurement of vector magnetic fields is critical for applications including navigation, geoscience, and space exploration. Nitrogen-vacancy (NV) center spin ensembles offer a promising solution for high-sensitivity vector magnetometry, as their different orientations in the diamond lattice measure different components of the magnetic field. However, the bias magnetic field typically used to separate signals from each NV orientation introduces inaccuracy from drifts in permanent magnet…

⏰ New study challenges assumptions linking racial attitudes and political identity in U.S. cities
https://phys.org/news/2025-06-assumptions-linking-racial-attitudes-political.html

New study challenges assumptions linking racial attitudes and political identity in U.S. cities
Nearly 40% of U.S. cities analyzed in a study in NPJ Complexity diverge from the common narrative that Republican-dominated areas have high levels of implicit racial bias while Democratic strongholds are more tolerant.

This https://arxiv.org/abs/2504.12252 has been replaced.
initial toot: https://mastoxiv.page/@a…

Electric field tunable spin-orbit gap in a bilayer graphene/WSe$_{2}$ quantum dot
We report on the investigation of proximity-induced spin-orbit coupling (SOC) in a heterostructure of bilayer graphene (BLG) and tungsten diselenide (WSe$_2$). A BLG quantum dot (QD) in the few-particle regime acts as a sensitive probe for induced SOC. Finite bias and magnetotransport spectroscopy measurements reveal a significantly enhanced SOC that decreases with the applied displacement field, distinguishing it from pristine BLG. Furthermore, our measurements demonstrate a reduced valley $g$…

This https://arxiv.org/abs/2408.08294 has been replaced.
link: https://scholar.google.com/scholar?q=a

eGAD! double descent is explained by Generalized Aliasing Decomposition
A central problem in data science is to use potentially noisy samples of an unknown function to predict values for unseen inputs. In classical statistics, predictive error is understood as a trade-off between the bias and the variance that balances model simplicity with its ability to fit complex functions. However, over-parameterized models exhibit counterintuitive behaviors, such as "double descent" in which models of increasing complexity exhibit decreasing generalization error. Others may e…

The Illusion of Fairness: Auditing Fairness Interventions with Audit Studies
Disa Sariola, Patrick Button, Aron Culotta, Nicholas Mattei
https://arxiv.org/abs/2507.02152

The Illusion of Fairness: Auditing Fairness Interventions with Audit Studies
Artificial intelligence systems, especially those using machine learning, are being deployed in domains from hiring to loan issuance in order to automate these complex decisions. Judging both the effectiveness and fairness of these AI systems, and their human decision making counterpart, is a complex and important topic studied across both computational and social sciences. Within machine learning, a common way to address bias in downstream classifiers is to resample the training data to offset…

Meta-Fair: AI-Assisted Fairness Testing of Large Language Models
Miguel Romero-Arjona, Jos\'e A. Parejo, Juan C. Alonso, Ana B. S\'anchez, Aitor Arrieta, Sergio Segura
https://arxiv.org/abs/2507.02533

Meta-Fair: AI-Assisted Fairness Testing of Large Language Models
Fairness--the absence of unjustified bias--is a core principle in the development of Artificial Intelligence (AI) systems, yet it remains difficult to assess and enforce. Current approaches to fairness testing in large language models (LLMs) often rely on manual evaluation, fixed templates, deterministic heuristics, and curated datasets, making them resource-intensive and difficult to scale. This work aims to lay the groundwork for a novel, automated method for testing fairness in LLMs, reducin…

Uncovering Bias Mechanisms in Observational Studies
Ilker Demirel, Zeshan Hussain, Piersilvio De Bartolomeis, David Sontag
https://arxiv.org/abs/2506.01191

Uncovering Bias Mechanisms in Observational Studies
Observational studies are a key resource for causal inference but are often affected by systematic biases. Prior work has focused mainly on detecting these biases, via sensitivity analyses and comparisons with randomized controlled trials, or mitigating them through debiasing techniques. However, there remains a lack of methodology for uncovering the underlying mechanisms driving these biases, e.g., whether due to hidden confounding or selection of participants. In this work, we show that the r…

This https://arxiv.org/abs/2503.05728 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCY_…

Political Neutrality in AI Is Impossible- But Here Is How to Approximate It
AI systems often exhibit political bias, influencing users' opinions and decisions. While political neutrality-defined as the absence of bias-is often seen as an ideal solution for fairness and safety, this position paper argues that true political neutrality is neither feasible nor universally desirable due to its subjective nature and the biases inherent in AI training data, algorithms, and user interactions. However, inspired by Joseph Raz's philosophical insight that "neutrality [...] can b…

This https://arxiv.org/abs/2503.14031 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Debiased Opto-electronic Joint Transform Correlator for Enhanced Real-Time Pattern Recognition
Opto-electronic joint transform correlators (OJTCs) use a focal plane array (FPA) to detect the joint power spectrum (JPS) of two input images, projecting it onto a spatial light modulator (SLM) to be optically Fourier transformed. The JPS is composed of two self-intensities and two conjugate-products, where only the latter produce the cross-correlation. However, the self-intensity terms are typically much stronger than the conjugate-products, producing a bias that consumes most of the availabl…

Analysis of LLM Bias (Chinese Propaganda & Anti-US Sentiment) in DeepSeek-R1 vs. ChatGPT o3-mini-high
PeiHsuan Huang, ZihWei Lin, Simon Imbot, WenCheng Fu, Ethan Tu
https://arxiv.org/abs/2506.01814

Analysis of LLM Bias (Chinese Propaganda & Anti-US Sentiment) in DeepSeek-R1 vs. ChatGPT o3-mini-high
Large language models (LLMs) increasingly shape public understanding and civic decisions, yet their ideological neutrality is a growing concern. While existing research has explored various forms of LLM bias, a direct, cross-lingual comparison of models with differing geopolitical alignments-specifically a PRC-system model versus a non-PRC counterpart-has been lacking. This study addresses this gap by systematically evaluating DeepSeek-R1 (PRC-aligned) against ChatGPT o3-mini-high (non-PRC) for…

Contextual Online Pricing with (Biased) Offline Data
Yixuan Zhang, Ruihao Zhu, Qiaomin Xie
https://arxiv.org/abs/2507.02762 https://a…

Contextual Online Pricing with (Biased) Offline Data
We study contextual online pricing with biased offline data. For the scalar price elasticity case, we identify the instance-dependent quantity $δ^2$ that measures how far the offline data lies from the (unknown) online optimum. We show that the time length $T$, bias bound $V$, size $N$ and dispersion $λ_{\min}(\hatΣ)$ of the offline data, and $δ^2$ jointly determine the statistical complexity. An Optimism-in-the-Face-of-Uncertainty (OFU) policy achieves a minimax-optimal, instance-dependent…

On the Language and Gender Biases in PSTN, VoIP and Neural Audio Codecs
Kemal Altwlkany, Amar Kuric, Emanuel Lacic
https://arxiv.org/abs/2506.02545 https:/…

On the Language and Gender Biases in PSTN, VoIP and Neural Audio Codecs
In recent years, there has been a growing focus on fairness and inclusivity within speech technology, particularly in areas such as automatic speech recognition and speech sentiment analysis. When audio is transcoded prior to processing, as is the case in streaming or real-time applications, any inherent bias in the coding mechanism may result in disparities. This not only affects user experience but can also have broader societal implications by perpetuating stereotypes and exclusion. Thus, it…

This https://arxiv.org/abs/2501.07071 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

Value Compass Leaderboard: A Platform for Fundamental and Validated Evaluation of LLMs Values
As Large Language Models (LLMs) achieve remarkable breakthroughs, aligning their values with humans has become imperative for their responsible development and customized applications. However, there still lack evaluations of LLMs values that fulfill three desirable goals. (1) Value Clarification: We expect to clarify the underlying values of LLMs precisely and comprehensively, while current evaluations focus narrowly on safety risks such as bias and toxicity. (2) Evaluation Validity: Existing …

This https://arxiv.org/abs/2411.15111 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csNE_…

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations
We investigate learnable activation functions in Physics-Informed Neural Networks (PINNs) for solving Partial Differential Equations (PDEs), comparing traditional Multilayer Perceptrons (MLPs) with fixed and trainable activations against Kolmogorov-Arnold Networks (KANs) that employ learnable basis functions. While PINNs effectively incorporate physical laws into the learning process, they suffer from convergence and spectral bias problems, which limit their applicability to problems with rapid…

Towards an Observational Detection of Halo Spin Bias using Spin-Orbit Coherence
Yigon Kim, Antonio D. Montero-Dorta, Rory Smith, Jong-Ho Shinn
https://arxiv.org/abs/2506.21827

Towards an Observational Detection of Halo Spin Bias using Spin-Orbit Coherence
Context. The clustering of dark-matter halos depends primarily on halo mass. However, at fixed halo mass, numerical simulations have revealed multiple secondary dependencies. This so-called secondary halo bias has important implications for our understanding of structure formation and observational cosmology. Despite its significance, the effect has not yet been measured observationally with statistical confidence. Aims. We aim to develop the first observational method to probe halo spin bias: …

MPF: Aligning and Debiasing Language Models post Deployment via Multi Perspective Fusion
Xin Guan, PeiHsin Lin, Zekun Wu, Ze Wang, Ruibo Zhang, Emre Kazim, Adriano Koshiyama
https://arxiv.org/abs/2507.02595

MPF: Aligning and Debiasing Language Models post Deployment via Multi Perspective Fusion
Multiperspective Fusion (MPF) is a novel posttraining alignment framework for large language models (LLMs) developed in response to the growing need for easy bias mitigation. Built on top of the SAGED pipeline, an automated system for constructing bias benchmarks and extracting interpretable baseline distributions, MPF leverages multiperspective generations to expose and align biases in LLM outputs with nuanced, humanlike baselines. By decomposing baseline, such as sentiment distributions from …

Trump signed an executive order decrying so-called anti-Christian bias in February,
and a hotline was created in April to encourage State Department workers to call in and snitch on their colleagues who might be acting with any kind of “anti-Christian bias.”
The Religious Liberty Commission was established via another executive order on May 1 during a flashy White House event.
Trump has even established his own Christian “faith” office in the White House, seemingly further…

Trump’s ‘anti-Christian bias’ taskforce reveals its propagandistic agenda in first meeting
Even though Christians are the dominant religious group in the country, Trump and his allies are desperate to portray persecution where there is none

Pseudorandom bits for non-commutative programs
Chin Ho Lee, Emanuele Viola
https://arxiv.org/abs/2506.01832 https://arxiv.org/pdf/250…

Pseudorandom bits for non-commutative programs
We obtain new explicit pseudorandom generators for several computational models involving groups. Our main results are as follows: 1. We consider read-once group-products over a finite group $G$, i.e., tests of the form $\prod_{i=1}^n g_i^{x_i}$ where $g_i\in G$, a special case of read-once permutation branching programs. We give generators with optimal seed length $c_G \log(n/\varepsilon)$ over any $p$-group. The proof uses the small-bias plus noise paradigm, but derandomizes the noise to av…

Disentangled Feature Importance
Jin-Hong Du, Kathryn Roeder, Larry Wasserman
https://arxiv.org/abs/2507.00260 https://arxiv.org/pdf/2…

Disentangled Feature Importance
Feature importance quantification faces a fundamental challenge: when predictors are correlated, standard methods systematically underestimate their contributions. We prove that major existing approaches target identical population functionals under squared-error loss, revealing why they share this correlation-induced bias. To address this limitation, we introduce \emph{Disentangled Feature Importance (DFI)}, a nonparametric generalization of the classical $R^2$ decomposition via optimal tran…

Covariate-informed link prediction with extreme taxonomic bias
Jennifer N. Kampe, Camille DeSisto, David B. Dunson
https://arxiv.org/abs/2506.23370 https:/…

Covariate-informed link prediction with extreme taxonomic bias
Biotic interactions provide a valuable window into the inner workings of complex ecological communities and capture the loss of ecological function often precipitated by environmental change. However, the financial and logistical challenges associated with collecting interaction data result in networks that are recorded with geographical and taxonomic bias, particularly when studies are narrowly focused. We develop an approach to reduce bias in link prediction in the common scenario in which da…

This https://arxiv.org/abs/2505.22984 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

A Computational Approach to Improving Fairness in K-means Clustering
The popular K-means clustering algorithm potentially suffers from a major weakness for further analysis or interpretation. Some cluster may have disproportionately more (or fewer) points from one of the subpopulations in terms of some sensitive variable, e.g., gender or race. Such a fairness issue may cause bias and unexpected social consequences. This work attempts to improve the fairness of K-means clustering with a two-stage optimization formulation--clustering first and then adjust cluster …

Quantifying the impact of detection bias from blended galaxies on cosmic shear surveys
Eray Genc, Peter Schneider, Sandra Unruh, Tim Schrabback
https://arxiv.org/abs/2507.01546

Quantifying the impact of detection bias from blended galaxies on cosmic shear surveys
Increasingly large areas in cosmic shear surveys lead to a reduction of statistical errors, necessitating to control systematic errors increasingly better. One of these systematic effects was initially studied by Hartlap et al. in 2011, namely that image overlap with (bright foreground) galaxies may prevent some distant (source) galaxies to remain undetected. Since this overlap is more likely to occur in regions of high foreground density -- which tend to be the regions in which the shear is la…

Towards Fair Rankings: Leveraging LLMs for Gender Bias Detection and Measurement
Maryam Mousavian, Zahra Abbasiantaeb, Mohammad Aliannejadi, Fabio Crestani
https://arxiv.org/abs/2506.22372

Towards Fair Rankings: Leveraging LLMs for Gender Bias Detection and Measurement
The presence of social biases in Natural Language Processing (NLP) and Information Retrieval (IR) systems is an ongoing challenge, which underlines the importance of developing robust approaches to identifying and evaluating such biases. In this paper, we aim to address this issue by leveraging Large Language Models (LLMs) to detect and measure gender bias in passage ranking. Existing gender fairness metrics rely on lexical- and frequency-based measures, leading to various limitations, e.g., mi…

Theories of "Sexuality" in Natural Language Processing Bias Research
Jacob Hobbs
https://arxiv.org/abs/2506.22481 https://a…

Theories of "Sexuality" in Natural Language Processing Bias Research
In recent years, significant advancements in the field of Natural Language Processing (NLP) have positioned commercialized language models as wide-reaching, highly useful tools. In tandem, there has been an explosion of multidisciplinary research examining how NLP tasks reflect, perpetuate, and amplify social biases such as gender and racial bias. A significant gap in this scholarship is a detailed analysis of how queer sexualities are encoded and (mis)represented by both NLP systems and practi…

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[1/5]:
- Combating Confirmation Bias: A Unified Pseudo-Labeling Framework for Entity Alignment
Qijie Ding, Jie Yin, Daokun Zhang, Junbin Gao

Benford's Curse: Tracing Digit Bias to Numerical Hallucination in LLMs
Jiandong Shao, Yao Lu, Jianfei Yang
https://arxiv.org/abs/2506.01734 https://

Benford's Curse: Tracing Digit Bias to Numerical Hallucination in LLMs
Large Language Models (LLMs) exhibit impressive performance on complex reasoning tasks, yet they frequently fail on basic numerical problems, producing incorrect outputs. Inspired by Benford's Law -- a statistical pattern where lower digits occur more frequently as leading digits -- we hypothesize that the long-tailed digit distributions in web-collected corpora may be learned by LLMs during pretraining, leading to biased numerical generation. To investigate the hypothesis, we first examine whe…

Bias, Accuracy, and Trust: Gender-Diverse Perspectives on Large Language Models
Aimen Gaba, Emily Wall, Tejas Ramkumar Babu, Yuriy Brun, Kyle Hall, Cindy Xiong Bearfield
https://arxiv.org/abs/2506.21898

Bias, Accuracy, and Trust: Gender-Diverse Perspectives on Large Language Models
Large language models (LLMs) are becoming increasingly ubiquitous in our daily lives, but numerous concerns about bias in LLMs exist. This study examines how gender-diverse populations perceive bias, accuracy, and trustworthiness in LLMs, specifically ChatGPT. Through 25 in-depth interviews with non-binary/transgender, male, and female participants, we investigate how gendered and neutral prompts influence model responses and how users evaluate these responses. Our findings reveal that gendered…

This https://arxiv.org/abs/2412.12405 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Generalized entropy calibration for analyzing voluntary survey data
Statistical analysis of voluntary survey data is an important area of research in survey sampling. We consider a unified approach to voluntary survey data analysis under the assumption that the sampling mechanism is ignorable. Generalized entropy calibration is introduced as a unified tool for calibration weighting to control the selection bias. We first establish the relationship between the generalized calibration weighting and its dual expression for regression estimation. The dual relations…

This https://arxiv.org/abs/2501.08718 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

Pixelization effects in cosmic shear angular power spectra
We conduct a comprehensive study into the impact of pixelization on cosmic shear, uncovering several sources of bias in standard pseudo-$C_\ell$ estimators based on discrete catalogues. We derive models that can bring residual biases to the percent level on small scales. We elucidate the impact of aliasing and the varying shape of HEALPix pixels on power spectra and show how the HEALPix pixel window function approximation is made in the discrete spin-2 setting. We propose several improvements t…

Ethical AI: Towards Defining a Collective Evaluation Framework
Aasish Kumar Sharma, Dimitar Kyosev, Julian Kunkel
https://arxiv.org/abs/2506.00233 https://…

Ethical AI: Towards Defining a Collective Evaluation Framework
Artificial Intelligence (AI) is transforming sectors such as healthcare, finance, and autonomous systems, offering powerful tools for innovation. Yet its rapid integration raises urgent ethical concerns related to data ownership, privacy, and systemic bias. Issues like opaque decision-making, misleading outputs, and unfair treatment in high-stakes domains underscore the need for transparent and accountable AI systems. This article addresses these challenges by proposing a modular ethical assess…

Bias vs Bias -- Dawn of Justice: A Fair Fight in Recommendation Systems
Tahsin Alamgir Kheya, Mohamed Reda Bouadjenek, Sunil Aryal
https://arxiv.org/abs/2506.18327

Bias vs Bias -- Dawn of Justice: A Fair Fight in Recommendation Systems
Recommendation systems play a crucial role in our daily lives by impacting user experience across various domains, including e-commerce, job advertisements, entertainment, etc. Given the vital role of such systems in our lives, practitioners must ensure they do not produce unfair and imbalanced recommendations. Previous work addressing bias in recommendations overlooked bias in certain item categories, potentially leaving some biases unaddressed. Additionally, most previous work on fair re-rank…

This https://arxiv.org/abs/2503.16456 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

Position: Beyond Assistance -- Reimagining LLMs as Ethical and Adaptive Co-Creators in Mental Health Care
This position paper argues for a fundamental shift in how Large Language Models (LLMs) are integrated into the mental health care domain. We advocate for their role as co-creators rather than mere assistive tools. While LLMs have the potential to enhance accessibility, personalization, and crisis intervention, their adoption remains limited due to concerns about bias, evaluation, over-reliance, dehumanization, and regulatory uncertainties. To address these challenges, we propose two structured …

This https://arxiv.org/abs/2112.02822 has been replaced.
link: https://scholar.google.com/scholar?q=a

A stableness of resistance model for nonresponse adjustment with callback data
Nonresponse arises frequently in surveys and follow-ups are routinely made to increase the response rate. In order to monitor the follow-up process, callback data have been used in social sciences and survey studies for decades. In modern surveys, the availability of callback data is increasing because the response rate is decreasing and follow-ups are essential to collect maximum information. Although callback data are helpful to reduce the bias in surveys, such data have not been widely used …

A Practical SAFE-AI Framework for Small and Medium-Sized Enterprises Developing Medical Artificial Intelligence Ethics Policies
Ion Nemteanu, Adir Mancebo Jr., Leslie Joe, Ryan Lopez, Patricia Lopez, Warren Woodrich Pettine
https://arxiv.org/abs/2507.01304

A Practical SAFE-AI Framework for Small and Medium-Sized Enterprises Developing Medical Artificial Intelligence Ethics Policies
Artificial intelligence (AI) offers incredible possibilities for patient care, but raises significant ethical issues, such as the potential for bias. Powerful ethical frameworks exist to minimize these issues, but are often developed for academic or regulatory environments and tend to be comprehensive but overly prescriptive, making them difficult to operationalize within fast-paced, resource-constrained environments. We introduce the Scalable Agile Framework for Execution in AI (SAFE-AI) desig…

Who's Sorry Now: User Preferences Among Rote, Empathic, and Explanatory Apologies from LLM Chatbots
Zahra Ashktorab, Alessandra Buccella, Jason D'Cruz, Zoe Fowler, Andrew Gill, Kei Yan Leung, P. D. Magnus, John Richards, Kush R. Varshney
https://arxiv.org/abs/2507.02745

Who's Sorry Now: User Preferences Among Rote, Empathic, and Explanatory Apologies from LLM Chatbots
As chatbots driven by large language models (LLMs) are increasingly deployed in everyday contexts, their ability to recover from errors through effective apologies is critical to maintaining user trust and satisfaction. In a preregistered study with Prolific workers (N=162), we examine user preferences for three types of apologies (rote, explanatory, and empathic) issued in response to three categories of common LLM mistakes (bias, unfounded fabrication, and factual errors). We designed a pairw…

Evaluating Scoring Bias in LLM-as-a-Judge
Qingquan Li, Shaoyu Dou, Kailai Shao, Chao Chen, Haixiang Hu
https://arxiv.org/abs/2506.22316 https://

Evaluating Scoring Bias in LLM-as-a-Judge
The remarkable performance of Large Language Models (LLMs) gives rise to``LLM-as-a-Judge'', where LLMs are employed as evaluators for complex tasks. Moreover, it has been widely adopted across fields such as Natural Language Processing (NLP), preference learning, and various specific domains. However, there are various biases within LLM-as-a-Judge, which adversely affect the fairness and reliability of judgments. Current research on evaluating or mitigating bias in LLM-as-a-Judge predominantly …

Disentangling the growth rate of perturbations from the HI bias using only clustering data from galaxy surveys
Pankaj Chavan, Tapomoy Guha Sarkar, Anjan A Sen
https://arxiv.org/abs/2506.22064

Disentangling the growth rate of perturbations from the HI bias using only clustering data from galaxy surveys
This work serves two-fold purpose. Firstly, we provide an alternative to the traditional method of determining the growth rate of density perturbations $f(z)$. In usual practice, $f(z)$ can not be directly measured from tracer clustering at some redshift without knowledge of the bias. While the bayron acoustic oscillation (BAO) imprint allows the determination of $(D_A(z), H(z))$, redshift space anisotropy (RSD) allows the measurement of a quantity $f_8(z) = f(z) σ_{8,0} D_{+}(z)$. To extract …

Leveraging In-Context Learning for Political Bias Testing of LLMs
Patrick Haller, Jannis Vamvas, Rico Sennrich, Lena A. J\"ager
https://arxiv.org/abs/2506.22232

Leveraging In-Context Learning for Political Bias Testing of LLMs
A growing body of work has been querying LLMs with political questions to evaluate their potential biases. However, this probing method has limited stability, making comparisons between models unreliable. In this paper, we argue that LLMs need more context. We propose a new probing task, Questionnaire Modeling (QM), that uses human survey data as in-context examples. We show that QM improves the stability of question-based bias evaluation, and demonstrate that it may be used to compare instruct…

Bias in estimating Theil, Atkinson, and dispersion indices for gamma mixture populations
Jackson Assis, Roberto Vila, Helton Saulo
https://arxiv.org/abs/2506.22168

Bias in estimating Theil, Atkinson, and dispersion indices for gamma mixture populations
In this paper, we derive closed-form expressions for the bias of estimators of the Theil, Atkinson, and dispersion indices when the underlying population follows a finite mixture of gamma distributions. Our methodology builds on probabilistic techniques grounded in Mosimann's proportion-sum independence theorem and the gamma-Dirichlet connection, enabling analytical tractability in the presence of population heterogeneity. These results extend existing findings for single gamma models.

Generative Exaggeration in LLM Social Agents: Consistency, Bias, and Toxicity
Jacopo Nudo, Mario Edoardo Pandolfo, Edoardo Loru, Mattia Samory, Matteo Cinelli, Walter Quattrociocchi
https://arxiv.org/abs/2507.00657

Generative Exaggeration in LLM Social Agents: Consistency, Bias, and Toxicity
We investigate how Large Language Models (LLMs) behave when simulating political discourse on social media. Leveraging 21 million interactions on X during the 2024 U.S. presidential election, we construct LLM agents based on 1,186 real users, prompting them to reply to politically salient tweets under controlled conditions. Agents are initialized either with minimal ideological cues (Zero Shot) or recent tweet history (Few Shot), allowing one-to-one comparisons with human replies. We evaluate t…

Multimodal Political Bias Identification and Neutralization
Cedric Bernard, Xavier Pleimling, Amun Kharel, Chase Vickery
https://arxiv.org/abs/2506.17372 h…

Multimodal Political Bias Identification and Neutralization
Due to the presence of political echo chambers, it becomes imperative to detect and remove subjective bias and emotionally charged language from both the text and images of political articles. However, prior work has focused on solely the text portion of the bias rather than both the text and image portions. This is a problem because the images are just as powerful of a medium to communicate information as text is. To that end, we present a model that leverages both text and image bias which co…

Unbiased estimation in new Gini index extensions under gamma distributions
Roberto Vila, Helton Saulo
https://arxiv.org/abs/2506.00666 https://

Unbiased estimation in new Gini index extensions under gamma distributions
In this paper, we propose two new flexible Gini indices (extended lower and upper) defined via differences between the $i$-th observation, the smallest order statistic, and the largest order statistic, for any $1 \leqslant i \leqslant m$. For gamma-distributed data, we obtain exact expectations of the estimators and establish their unbiasedness, generalizing prior works by [Deltas, G. 2003. The small-sample bias of the gini coefficient: Results and implications for empirical research. Review of…

Replaced article(s) found for cs.CL. https://arxiv.org/list/cs.CL/new
[1/4]:
- Bias-Augmented Consistency Training Reduces Biased Reasoning in Chain-of-Thought
James Chua, Edward Rees, Hunar Batra, Samuel R. Bowman, Julian Michael, Ethan Perez, Miles Turpin

Targeted tuning of random forests for quantile estimation and prediction intervals
Matthew Berkowitz, Rachel MacKay Altman, Thomas M. Loughin
https://arxiv.org/abs/2507.01430

Targeted tuning of random forests for quantile estimation and prediction intervals
We present a novel tuning procedure for random forests (RFs) that improves the accuracy of estimated quantiles and produces valid, relatively narrow prediction intervals. While RFs are typically used to estimate mean responses (conditional on covariates), they can also be used to estimate quantiles by estimating the full distribution of the response. However, standard approaches for building RFs often result in excessively biased quantile estimates. To reduce this bias, our proposed tuning proc…

Intersectional Bias in Japanese Large Language Models from a Contextualized Perspective
Hitomi Yanaka, Xinqi He, Jie Lu, Namgi Han, Sunjin Oh, Ryoma Kumon, Yuma Matsuoka, Katsuhiko Watabe, Yuko Itatsu
https://arxiv.org/abs/2506.12327

Intersectional Bias in Japanese Large Language Models from a Contextualized Perspective
An growing number of studies have examined the social bias of rapidly developed large language models (LLMs). Although most of these studies have focused on bias occurring in a single social attribute, research in social science has shown that social bias often occurs in the form of intersectionality -- the constitutive and contextualized perspective on bias aroused by social attributes. In this study, we construct the Japanese benchmark inter-JBBQ, designed to evaluate the intersectional bias …

SACL: Understanding and Combating Textual Bias in Code Retrieval with Semantic-Augmented Reranking and Localization
Dhruv Gupta, Gayathri Ganesh Lakshmy, Yiqing Xie
https://arxiv.org/abs/2506.20081

SACL: Understanding and Combating Textual Bias in Code Retrieval with Semantic-Augmented Reranking and Localization
Retrieval-Augmented Code Generation (RACG) is a critical technique for enhancing code generation by retrieving relevant information. In this work, we conduct an in-depth analysis of code retrieval by systematically masking specific features while preserving code functionality. Our discoveries include: (1) although trained on code, current retrievers heavily rely on surface-level textual features (e.g., docstrings, identifier names), and (2) they exhibit a strong bias towards well-documented cod…