Tootfinder

President Trump's AI Action Plan, which has 90 policy recommendations, explicitly seeks to roll back Biden-era measures on AI bias and cybersecurity threats (Kate Knibbs/Wired)
https://www.wired.com/story/trumps-ai-action-plan-crusade-against-bias-regu…

Trump’s AI Action Plan Is a Crusade Against ‘Bias’—and Regulation
The Trump administration’s new AI policy blueprint calls for limited regulation and lots of leeway for Big Tech to grow even bigger.

Obscured but Not Erased: Evaluating Nationality Bias in LLMs via Name-Based Bias Benchmarks
Giulio Pelosio, Devesh Batra, No\'emie Bovey, Robert Hankache, Cristovao Iglesias, Greig Cowan, Raad Khraishi
https://arxiv.org/abs/2507.16989

Obscured but Not Erased: Evaluating Nationality Bias in LLMs via Name-Based Bias Benchmarks
Large Language Models (LLMs) can exhibit latent biases towards specific nationalities even when explicit demographic markers are not present. In this work, we introduce a novel name-based benchmarking approach derived from the Bias Benchmark for QA (BBQ) dataset to investigate the impact of substituting explicit nationality labels with culturally indicative names, a scenario more reflective of real-world LLM applications. Our novel approach examines how this substitution affects both bias magni…

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…

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…

Skydance tells the US FCC that once its Paramount Global acquisition is complete, it will initiate a "comprehensive review" of CBS, hire an ombudsman, and more (Alex Weprin/The Hollywood Reporter)
https://www.hollywoodreporter.com/business…

Skydance Tells FCC It Will Create CBS News Ombudsman, Commits to “Comprehensive Review” of Network Post-Transaction
The company also said that it would eliminate all DEI initiatives at Paramount.

Steering Conceptual Bias via Transformer Latent-Subspace Activation
Vansh Sharma, Venkat Raman
https://arxiv.org/abs/2506.18887 https://

Steering Conceptual Bias via Transformer Latent-Subspace Activation
This work examines whether activating latent subspaces in language models (LLMs) can steer scientific code generation toward a specific programming language. Five causal LLMs were first evaluated on scientific coding prompts to quantify their baseline bias among four programming languages. A static neuron-attribution method, perturbing the highest activated MLP weight for a C++ or CPP token, proved brittle and exhibited limited generalization across prompt styles and model scales. To address th…

About That Japan Deal -- Arithmetic has a well-known globalist bias (Paul Krugman)
https://paulkrugman.substack.com/p/about-that-japan-deal
http://www.memeorandum.com/250723/p35#a250723p35

KI-Update kompakt: LLMs für Malware, Anti-Human-Bias, Sutton, Chatbots
Das "KI-Update" liefert werktäglich eine Zusammenfassung der wichtigsten KI-Entwicklungen.
https://www.

The Trump administration plans to vet AI models for "ideological bias" and limit government contracts to tech companies whose models offer "objective truth" (Financial Times)
https://www.ft.com/content/406bc127-e1c3-41d5-9e68-b8921856c3c7

Bias Variation Compensation in Perimeter-Gated SPAD TRNGs
Md Sakibur Sajal, Hunter Guthrie, Marc Dandin
https://arxiv.org/abs/2506.15888 https://

Bias Variation Compensation in Perimeter-Gated SPAD TRNGs
Random number generators that utilize arrays of entropy source elements suffer from bias variation (BV). Despite the availability of efficient debiasing algorithms, optimized implementations of hardware friendly options depend on the bit bias in the raw bit streams and cannot accommodate a wide BV. In this work, we present a 64 x 64 array of perimeter gated single photon avalanche diodes (pgSPADs), fabricated in a 0.35 μm standard CMOS technology, as a source of entropy to generate random bina…

Multivariate Statistical Analysis of Exoplanet Habitability: Detection Bias and Earth Analog Identification
Caleb Traxler, Samuel Townsend, Abby Mori, Grace Newman, Kaitlyn Morenzone
https://arxiv.org/abs/2506.18200

Multivariate Statistical Analysis of Exoplanet Habitability: Detection Bias and Earth Analog Identification
We present a comprehensive multivariate statistical analysis of 517 exoplanets from the NASA Exoplanet Archive to identify potentially habitable worlds and quantify detection bias in current surveys. Using eight key parameters (planetary radius, equilibrium temperature, insolation flux, density, and stellar effective temperature, radius, mass, metallicity), we developed a classification framework that successfully identifies Earth as an "Excellent Candidate" for habitability. Our analysis revea…

Undersmoothed LASSO Models for Propensity Score Weighting and Synthetic Negative Control Exposures for Bias Detection
Richard Wyss, Ben B. Hansen, Georg Hahn, Lars van der Laan, Joshua K. Lin
https://arxiv.org/abs/2506.17760

Undersmoothed LASSO Models for Propensity Score Weighting and Synthetic Negative Control Exposures for Bias Detection
The propensity score (PS) is often used to control for large numbers of covariates in high-dimensional healthcare database studies. The least absolute shrinkage and selection operator (LASSO) is a data-adaptive prediction algorithm that has become the most widely used tool for large-scale PS estimation in these settings. However, recent work has shown that the use of data-adaptive algorithms for PS estimation can come at the cost of slow convergence rates, resulting in PS-based causal estimator…

Bias hardened estimators of patchy screening profiles
Noah Sailer, Boryana Hadzhiyska, Simone Ferraro
https://arxiv.org/abs/2506.17217 https://

Bias hardened estimators of patchy screening profiles
Detecting anisotropic screening of the cosmic microwave background (CMB) holds the promise of revealing the distribution of gas in the Universe, characterizing the complex processes of galaxy formation and feedback, and studying the epoch of reionization. Estimators for inhomogeneous screening, including some recently proposed small-scale (stacked) estimators, are quadratic or higher order in the CMB temperature or polarization fields and are therefore subject to contamination from CMB lensing.…

EvoFormer: Learning Dynamic Graph-Level Representations with Structural and Temporal Bias Correction
Haodi Zhong, Liuxin Zou, Di Wang, Bo Wang, Zhenxing Niu, Quan Wang
https://arxiv.org/abs/2508.15378 …

EvoFormer: Learning Dynamic Graph-Level Representations with Structural and Temporal Bias Correction
Dynamic graph-level embedding aims to capture structural evolution in networks, which is essential for modeling real-world scenarios. However, existing methods face two critical yet under-explored issues: Structural Visit Bias, where random walk sampling disproportionately emphasizes high-degree nodes, leading to redundant and noisy structural representations; and Abrupt Evolution Blindness, the failure to effectively detect sudden structural changes due to rigid or overly simplistic temporal m…

#SteadyCommunityContent #KINews #Retröt
Nach einigen Monaten Wartezeit habe ich nochmals DALL-E via

ChatGPT 4 vs. 4o: Geschlechterklischees in der Bildgenerierung
Wie beeinflussen Bias und Stereotype die KI-Bildgenerierung? Eine Analyse von DALLE und seinen Stereotypen zu Geschlechtern und Berufen.

Position Bias Mitigates Position Bias:Mitigate Position Bias Through Inter-Position Knowledge Distillation
Yifei Wang, Feng Xiong, Yong Wang, Linjing Li, Xiangxiang Chu, Daniel Dajun Zeng
https://arxiv.org/abs/2508.15709

Position Bias Mitigates Position Bias:Mitigate Position Bias Through Inter-Position Knowledge Distillation
Positional bias (PB), manifesting as non-uniform sensitivity across different contextual locations, significantly impairs long-context comprehension and processing capabilities. While prior work seeks to mitigate PB through modifying the architectures causing its emergence, significant PB still persists. To address PB effectively, we introduce \textbf{Pos2Distill}, a position to position knowledge distillation framework. Pos2Distill transfers the superior capabilities from advantageous position…

Trustworthy Artificial Intelligence for Cyber Threat Analysis
Shuangbao Paul Wang, Paul Mullin
https://arxiv.org/abs/2506.19052 https://

Trustworthy Artificial Intelligence for Cyber Threat Analysis
Artificial Intelligence brings innovations into the society. However, bias and unethical exist in many algorithms that make the applications less trustworthy. Threats hunting algorithms based on machine learning have shown great advantage over classical methods. Reinforcement learning models are getting more accurate for identifying not only signature-based but also behavior-based threats. Quantum mechanics brings a new dimension in improving classification speed with exponential advantage. In …

SDDiff: Boost Radar Perception via Spatial-Doppler Diffusion
Shengpeng Wang, Xin Luo, Yulong Xie, Wei Wang
https://arxiv.org/abs/2506.16936 https://…

SDDiff: Boost Radar Perception via Spatial-Doppler Diffusion
Point cloud extraction (PCE) and ego velocity estimation (EVE) are key capabilities gaining attention in 3D radar perception. However, existing work typically treats these two tasks independently, which may neglect the interplay between radar's spatial and Doppler domain features, potentially introducing additional bias. In this paper, we observe an underlying correlation between 3D points and ego velocity, which offers reciprocal benefits for PCE and EVE. To fully unlock such inspiring potenti…

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Randomization Can Reduce Both Bias and Variance: A Case Study in Random Forests
Brian Liu, Rahul Mazumder

X refuses to cooperate with French probe into algorithm bias
https://www.lemonde.fr/en/pixels/article/2025/07/21/x-refuses-to-cooperate-with-french-probe-into-algorithm-bias_6743577_13.html

Social Group Bias in AI Finance
Thomas R. Cook, Sophia Kazinnik
https://arxiv.org/abs/2506.17490 https://arxiv.org/pdf/2506.17490

Social Group Bias in AI Finance
Financial institutions increasingly rely on large language models (LLMs) for high-stakes decision-making. However, these models risk perpetuating harmful biases if deployed without careful oversight. This paper investigates racial bias in LLMs specifically through the lens of credit decision-making tasks, operating on the premise that biases identified here are indicative of broader concerns across financial applications. We introduce a reproducible, counterfactual testing framework that evalua…

Bimodal distribution of delay times and splitting of the zero-bias conductance peak in a double-barrier normal-superconductor junction
C. W. J. Beenakker, V. A. Zakharov
https://arxiv.org/abs/2506.18515

Bimodal distribution of delay times and splitting of the zero-bias conductance peak in a double-barrier normal-superconductor junction
We formulate a scattering theory of the proximity effect in a weakly disordered SININ junction (S = superconductor, I = insulating barrier, N = normal metal). This allows to relate the conductance and density of states of the junction to the scattering times $τ$ (eigenvalues of the Wigner-Smith time-delay matrix). The probability density $P(τ)$ has two peaks, at a short time $τ_{\rm min}$ and a late time $τ_{\rm max}$. The density of states at the Fermi level is the geometric mean of the tw…

FCC's Brendan Carr says he is pleased with some of Skydance's proposed concessions for its Paramount merger, including addressing complaints of news bias at CBS (Kelcee Griffis/Bloomberg)
https://www.bloomberg.com/news/articles/20

LLM-empowered Dynamic Prompt Routing for Vision-Language Models Tuning under Long-Tailed Distributions
Yongju Jia, Jiarui Ma, Xiangxian Li, Baiqiao Zhang, Xianhui Cao, Juan Liu, Yulong Bian
https://arxiv.org/abs/2508.15688

LLM-empowered Dynamic Prompt Routing for Vision-Language Models Tuning under Long-Tailed Distributions
Pre-trained vision-language models (VLMs), such as CLIP, have demonstrated impressive capability in visual tasks, but their fine-tuning often suffers from bias in class-imbalanced scene. Recent works have introduced large language models (LLMs) to enhance VLM fine-tuning with supplementing semantic information. However, they often overlook inherent class imbalance in VLMs' pre-training, which may lead to bias accumulation in downstream tasks. To address this problem, this paper proposes a Multi…

Impact of Detector Calibration Accuracy on Black Hole Spectroscopy
Mallika R. Sinha, Ling Sun, Sizheng Ma
https://arxiv.org/abs/2506.15979 https://

Impact of Detector Calibration Accuracy on Black Hole Spectroscopy
Systematic errors in detector calibration can bias signal analyses and potentially lead to incorrect interpretations suggesting violations of general relativity. In this study, we investigate how calibration systematics affect black hole (BH) spectroscopy, a technique that uses the quasinormal modes (QNMs) emitted during the ringdown phase of gravitational waves (GWs) to study remnant BHs formed in compact binary coalescences. We simulate a series of physically motivated, tunable calibration er…

Conservative data-driven finite element formulation
Adriana Kulikov\'a (Glasgow Computational Engineering Centre), Andrei G. Shvarts (Glasgow Computational Engineering Centre), {\L}ukasz Kaczmarczyk (Glasgow Computational Engineering Centre), Chris J. Pearce (Glasgow Computational Engineering Centre)
https://arxiv.org/abs/25…

Conservative data-driven finite element formulation
This paper presents a new data-driven finite element framework derived with mixed finite element formulation. The standard approach to diffusion problems requires the solution of the mathematical equations that describe both the conservation law and the constitutive relations, where the latter is traditionally obtained after fitting experimental data to simplified material models. To exploit all available information and avoid bias in the material model, we follow a data-driven approach. While …

At an All-In Podcast summit, President Trump said forcing AI firms to pay for each copyrighted work is "not doable", calling for "common sense" AI and IP rules (Deadline)
https://deadline.com/2025/07/trump-ai-action-plan-copyright-1236466617/

White House’s AI Action Plan Includes Rules On Ideological Bias And Calls For Removing Regulatory Barriers, But Makes No Comment On Copyright
The plan does not address the use of copyrighted material in training models.

Ferroelectric Nematic Liquid Crystal-Based Silicon Photonic Modulator Demonstrated at 102 Gbit/s PAM-4
Li-Yuan Chiang, Gianlorenzo Masini, Rih-You Chen, Chirag Patel, Pavel Savechenkov, Yi-Jen Chiu, Cory Pecinovsky, Jason W. Sickler
https://arxiv.org/abs/2507.14724

Ferroelectric Nematic Liquid Crystal-Based Silicon Photonic Modulator Demonstrated at 102 Gbit/s PAM-4
We present the first demonstration of a hybrid integrated silicon and ferroelectric nematic liquid crystal modulator achieving 102~Gbit per second PAM-4 modulation. Operating within the C-band at a 3.5 V DC bias, this Pockels-based Mach-Zehnder modulator exhibits a $V_πL_\mathrm{AC}$ of 0.3 V$\cdot$cm.

Restricted Boltzmann machine as a probabilistic Enigma
Bin Chen, Weichao Yu
https://arxiv.org/abs/2507.17236 https://arxiv.org/pdf/2507.17236

Restricted Boltzmann machine as a probabilistic Enigma
We theoretically propose a symmetric encryption scheme based on Restricted Boltzmann Machines that functions as a probabilistic Enigma device, encoding information in the marginal distributions of visible states while utilizing bias permutations as cryptographic keys. Theoretical analysis reveals significant advantages including factorial key space growth through permutation matrices, excellent diffusion properties, and computational complexity rooted in sharp P-complete problems that resist qu…

Machine learning-based multimodal prognostic models integrating pathology images and high-throughput omic data for overall survival prediction in cancer: a systematic review
Charlotte Jennings (National Pathology Imaging Cooperative, Leeds Teaching Hospitals NHS Trust, Leeds, UK), Andrew Broad (National Pathology Imaging Cooperative, Leeds Teaching Hospitals NHS Trust, Leeds, UK), Lucy Godson (National Pathology Imaging Cooperative, Leeds Teaching Hospitals NHS Trust, Leeds, UK), Emily…

Machine learning-based multimodal prognostic models integrating pathology images and high-throughput omic data for overall survival prediction in cancer: a systematic review
Multimodal machine learning integrating histopathology and molecular data shows promise for cancer prognostication. We systematically reviewed studies combining whole slide images (WSIs) and high-throughput omics to predict overall survival. Searches of EMBASE, PubMed, and Cochrane CENTRAL (12/08/2024), plus citation screening, identified eligible studies. Data extraction used CHARMS; bias was assessed with PROBAST+AI; synthesis followed SWiM and PRISMA 2020. Protocol: PROSPERO (CRD42024594745)…

Defect-Mediated Aggregation and Motility-Induced Phase Separation in Active XY Model
Shun Inoue, Satoshi Yukawa
https://arxiv.org/abs/2507.17108 https://ar…

Defect-Mediated Aggregation and Motility-Induced Phase Separation in Active XY Model
We propose an ``active XY model'' that incorporates key elements of both the classical XY model and the Vicsek model to study the role of topological defects in active matter systems. This model features self-propelled particles with XY spin degrees of freedom on a lattice and introduces a self-propulsion parameter controlling the directional bias of particle motion. Using numerical simulations, we demonstrate that self-propulsion induces motility-induced phase separation (MIPS), where particle…

Our results confirm persistent gender disparities:
women are more frequently acknowledged than credited as co-authors, especially in roles involving investigation and analysis
https://arxiv.org/abs/2506.15237

Dissecting the gender divide: Authorship and acknowledgment in scientific publications
The issue of gender bias in scientific publications has been the subject of ongoing debate. One aspect of this debate concerns whether women receive equal credit for their contributions compared to men. Conventional wisdom suggests that women are more likely to be acknowledged than listed as co-authors, a role that carries greater prestige. Here, we analyze data from hundreds of thousands of scientists across nine disciplines and a broad range of publications. Our results confirm persistent gen…

Replaced article(s) found for econ.EM. https://arxiv.org/list/econ.EM/new
[1/1]:
- Assessing Omitted Variable Bias when the Controls are Endogenous
Paul Diegert, Matthew A. Masten, Alexandre Poirier

Quenched scaling limit for biased random walks on random, heavy tailed conductances: low dimensions
Umberto De Ambroggio, Carlo Scali
https://arxiv.org/abs/2507.17583 https://…

Quenched scaling limit for biased random walks on random, heavy tailed conductances: low dimensions
We consider a random walk amongst positive random conductances on $\mathbb{Z}^d, d \ge 2$, with directional bias. When the conductances have a stable distribution with parameter $γ\in (0, 1)$, the walk is sub-ballistic. In this regime Fribergh and Kious (Ann. Prob. 2018) derived an annealed scaling limit for the appropriately rescaled walk towards the Fractional Kinetics process. We prove the quenched version of this result for all $d \ge 2$.

Addressing Bias in Algorithmic Solutions: Exploring Vertex Cover and Feedback Vertex Set
Sheikh Shakil Akhtar, Jayakrishnan Madathil, Pranabendu Misra, Geevarghese Philip
https://arxiv.org/abs/2507.14509

Addressing Bias in Algorithmic Solutions: Exploring Vertex Cover and Feedback Vertex Set
A typical goal of research in combinatorial optimization is to come up with fast algorithms that find optimal solutions to a computational problem. The process that takes a real-world problem and extracts a clean mathematical abstraction of it often throws out a lot of "side information" which is deemed irrelevant. However, the discarded information could be of real significance to the end-user of the algorithm's output. All solutions of the same cost are not necessarily of equal impact in the …

Stabilization of the bias point in MZM modulators
Zhuo Wang
https://arxiv.org/abs/2507.14888 https://arxiv.org/pdf/2507.14888

Stabilization of the bias point in MZM modulators
This article mainly introduces the role of MZM in practical communication systems, the materials used to make MZM modulators such as lithium niobate, and its working principle. It also explains why it changes due to environmental factors. This leads to the introduction of a method that controls the stable points of MZM by algorithmically controlling the voltage, and the algorithm is verified through experiments. Finally, a summary and outlook on the future development of MZM are provided.

AI Through the Human Lens: Investigating Cognitive Theories in Machine Psychology
Akash Kundu, Rishika Goswami
https://arxiv.org/abs/2506.18156 https://

AI Through the Human Lens: Investigating Cognitive Theories in Machine Psychology
We investigate whether Large Language Models (LLMs) exhibit human-like cognitive patterns under four established frameworks from psychology: Thematic Apperception Test (TAT), Framing Bias, Moral Foundations Theory (MFT), and Cognitive Dissonance. We evaluated several proprietary and open-source models using structured prompts and automated scoring. Our findings reveal that these models often produce coherent narratives, show susceptibility to positive framing, exhibit moral judgments aligned wi…

Reading Between the Lines: A Study of Thematic Bias in Book Recommender Systems
Nityaa Kalra, Savvina Daniil
https://arxiv.org/abs/2508.15643 https://arxiv…

Reading Between the Lines: A Study of Thematic Bias in Book Recommender Systems
Recommender systems help users discover new content, but can also reinforce existing biases, leading to unfair exposure and reduced diversity. This paper introduces and investigates thematic bias in book recommendations, defined as a disproportionate favouring or neglect of certain book themes. We adopt a multi-stage bias evaluation framework using the Book-Crossing dataset to evaluate thematic bias in recommendations and its impact on different user groups. Our findings show that thematic bi…

The Impact of $\Omega_{m0}$ Prior Bias on Cosmological Parameter Estimation: Reconciling DESI DR2 BAO and Pantheon SNe Data Combination Results
Seokcheon Lee
https://arxiv.org/abs/2506.16022

The Impact of $Ω_{m0}$ Prior Bias on Cosmological Parameter Estimation: Reconciling DESI DR2 BAO and Pantheon+ SNe Data Combination Results
Recent cosmological parameter analyses combining DESI DR2 Baryon Acoustic Oscillation (BAO) data with external probes, such as Pantheon+ Supernovae (SNe) observations, have reported deviations of the dark energy equation-of-state parameters ($\oo, \oa$) from the standard $Λ$CDM model predictions ($\oo=-1, \oa=0$). A notable aspect of these results is the role of $\Omo$ prior information from SNe, which is known to exhibit tension with BAO-only constraints. In this study, we rigorously investig…

Assessing robustness and bias in 1D retrievals of 3D Global Circulation Models at high spectral resolution: a WASP-76 b simulation case study in emission
Lennart van Sluijs, Hayley Beltz, Isaac Malsky, Genevieve H. Pereira, L. Cinque, Emily Rauscher, Jayne Birkby
https://arxiv.org/abs/2507.16687

Assessing robustness and bias in 1D retrievals of 3D Global Circulation Models at high spectral resolution: a WASP-76 b simulation case study in emission
High-resolution spectroscopy (HRS) of exoplanet atmospheres has successfully detected many chemical species and is quickly moving toward detailed characterization of the chemical abundances and dynamics. HRS is highly sensitive to the line shape and position, thus, it can detect three-dimensional (3D) effects such as winds, rotation, and spatial variation of atmospheric conditions. At the same time, retrieval frameworks are increasingly deployed to constrain chemical abundances, pressure-temper…

Divide or Confer: Aggregating Information without Verification
James Best, Daniel Quigley, Maryam Saeedi, Ali Shourideh
https://arxiv.org/abs/2507.17609 https://

Divide or Confer: Aggregating Information without Verification
We examine receiver-optimal mechanisms for aggregating information that is divided across many biased senders. Each sender privately observes an unconditionally independent signal about an unknown state, so no sender can verify another's report. A receiver has a binary accept/reject decision which determines players' payoffs via the state. When information is divided across a small population and bias is low, the receiver-optimal mechanism coincides with the sender-preferred allocation, and can…

KI-Systeme bevorzugen eigene Texte: Studie warnt vor "Anti-Human-Bias"
Wissenschaftler haben nachgewiesen, dass Large Language Models Inhalte anderer KI-Systeme bevorzugen. Was Menschen daraus lernen können.

Kinematic Model Optimization via Differentiable Contact Manifold for In-Space Manipulation
Abhay Negi, Omey M. Manyar, Satyandra K. Gupta
https://arxiv.org/abs/2506.17458

Kinematic Model Optimization via Differentiable Contact Manifold for In-Space Manipulation
Robotic manipulation in space is essential for emerging applications such as debris removal and in-space servicing, assembly, and manufacturing (ISAM). A key requirement for these tasks is the ability to perform precise, contact-rich manipulation under significant uncertainty. In particular, thermal-induced deformation of manipulator links and temperature-dependent encoder bias introduce kinematic parameter errors that significantly degrade end-effector accuracy. Traditional calibration techniq…

I need you to think about how bias happens in the real world.
Like the banality of evil, bias isn't often perpetrated by monsters, rather by well meaning people who think they're being fair and thoughtful and doing their best — and they (you! us!), we ARE doing our best, but I need you to understand that doing our best isn't good enough to identify, understand, prevent, nor heal all harm.

The Impact of Pseudo-Science in Financial Loans Risk Prediction
Bruno Scarone, Ricardo Baeza-Yates
https://arxiv.org/abs/2507.16182 https://arxiv.org/pdf/2…

The Impact of Pseudo-Science in Financial Loans Risk Prediction
We study the societal impact of pseudo-scientific assumptions for predicting the behavior of people in a straightforward application of machine learning to risk prediction in financial lending. This use case also exemplifies the impact of survival bias in loan return prediction. We analyze the models in terms of their accuracy and social cost, showing that the socially optimal model may not imply a significant accuracy loss for this downstream task. Our results are verified for commonly used le…

Transfer entropy for finite data
Alec Kirkley
https://arxiv.org/abs/2506.16215 https://arxiv.org/pdf/2506.16215

Transfer entropy for finite data
Transfer entropy is a widely used measure for quantifying directed information flows in complex systems. While the challenges of estimating transfer entropy for continuous data are well known, it has two major shortcomings that persist even for data of finite cardinality: it exhibits a substantial positive bias for sparse bin counts, and it has no clear means to assess statistical significance. By more precisely accounting for information content in finite data streams, we derive a transfer ent…

“Developers on teams with high AI adoption complete 21% more tasks and merge 98% more pull requests, but PR review time increases 91%, revealing a critical bottleneck: human approval.“
Maybe it’s confirmation bias, but I can see that. You generate more, maybe harder to comprehend, code that still has to be double checked by people who weren’t involved in the process. That slows you down unless you ignore understanding by, you guessed it, moving fast and breaking things.

The AI Productivity Paradox Research Report | Faros AI
Key findings from the AI Productivity Paradox Report 2025. Research reveals AI coding assistants increase developer output, but not company productivity. Uncover strategies and enablers for a measurable return on investment.

State of Abdominal CT Datasets: A Critical Review of Bias, Clinical Relevance, and Real-world Applicability
Saeide Danaei, Zahra Dehghanian, Elahe Meftah, Nariman Naderi, Seyed Amir Ahmad Safavi-Naini, Faeze Khorasanizade, Hamid R. Rabiee
https://arxiv.org/abs/2508.13626

State of Abdominal CT Datasets: A Critical Review of Bias, Clinical Relevance, and Real-world Applicability
This systematic review critically evaluates publicly available abdominal CT datasets and their suitability for artificial intelligence (AI) applications in clinical settings. We examined 46 publicly available abdominal CT datasets (50,256 studies). Across all 46 datasets, we found substantial redundancy (59.1\% case reuse) and a Western/geographic skew (75.3\% from North America and Europe). A bias assessment was performed on the 19 datasets with >=100 cases; within this subset, the most preval…

How Robust is Model Editing after Fine-Tuning? An Empirical Study on Text-to-Image Diffusion Models
Feng He, Zhenyang Liu, Marco Valentino, Zhixue Zhao
https://arxiv.org/abs/2506.18428

How Robust is Model Editing after Fine-Tuning? An Empirical Study on Text-to-Image Diffusion Models
Model editing offers a low-cost technique to inject or correct a particular behavior in a pre-trained model without extensive retraining, supporting applications such as factual correction and bias mitigation. Despite this common practice, it remains unknown whether edits persist after fine-tuning or whether they are inadvertently reversed. This question has fundamental practical implications. For example, if fine-tuning removes prior edits, it could serve as a defence mechanism against hidden …

Improving precision of cumulative incidence estimates in randomized controlled trials with external controls
Zehao Su, Helene C. W. Rytgaard, Henrik Ravn, Frank Eriksson
https://arxiv.org/abs/2506.18415

Improving precision of cumulative incidence estimates in randomized controlled trials with external controls
Augmenting the control arm in clinical trials with external data can improve statistical power for demonstrating treatment effects. In many time-to-event outcome trials, participants are subject to truncation by death. Direct application of methods for competing risks analysis on the joint data may introduce bias, for example, due to covariate shifts between the populations. In this work, we consider transportability of the conditional cause-specific hazard of the event of interest under the co…

from my link log —
Toxic Origins, Toxic Decisions: bias in CEO selection towards risk-taking, analysed using polluted Superfund sites.
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5270031
saved 2025-05-31

Replaced article(s) found for cs.CL. https://arxiv.org/list/cs.CL/new
[2/5]:
- ASCenD-BDS: Adaptable, Stochastic and Context-aware framework for Detection of Bias, Discriminati...
Rajiv Bahl, et al.

Improving Fairness in Graph Neural Networks via Counterfactual Debiasing
Zengyi Wo, Chang Liu, Yumeng Wang, Minglai Shao, Wenjun Wang
https://arxiv.org/abs/2508.14683 https://…

Improving Fairness in Graph Neural Networks via Counterfactual Debiasing
Graph Neural Networks (GNNs) have been successful in modeling graph-structured data. However, similar to other machine learning models, GNNs can exhibit bias in predictions based on attributes like race and gender. Moreover, bias in GNNs can be exacerbated by the graph structure and message-passing mechanisms. Recent cutting-edge methods propose mitigating bias by filtering out sensitive information from input or representations, like edge dropping or feature masking. Yet, we argue that such st…

Omnidirectionally manipulated skyrmions in an orientationally chiral system
Jiahao Chen, Wentao Tang, Xingzhou Tang, Yang Ding, Jie Ni, Yuxi Chen, Bingxiang Li, Rui Zhang, Juan de Pablo, Yanqing Lu
https://arxiv.org/abs/2506.16781

Omnidirectionally manipulated skyrmions in an orientationally chiral system
Skyrmions, originally from condensed matter physics, have been widely explored in various physical systems, including soft matter. A crucial challenge in manipulating topological solitary waves like skyrmions is controlling their flow on demand. Here, we control the arbitrary moving direction of skyrmions in a chiral liquid crystal system by adjusting the bias of the applied alternate current electric field. Specifically, the velocity, including both moving direction and speed can be continuous…

A meta-analysis of impact factors of particle physics journals using NASA/ADS
Rayani Venkat Sai Rithvik, Shantanu Desai
https://arxiv.org/abs/2507.14806 ht…

A meta-analysis of impact factors of particle physics journals using NASA/ADS
In this work, we calculate the 2025 impact factors for 16 journals in Particle Physics (and related areas such as Nuclear Physics) using citations collated by NASA/ADS (Astrophysics Data System). We then compare them to the official impact factors calculated by Clarivate. We also compare these impact factors to the median-based impact factors introduced in a previous work. We do not find any systematic bias between the ADS and official impact factors. We find the maximum relative difference bet…

Intertwined magnetic phase driven exchange bias and its impact on the anomalous Hall effect in MnBi$_4$Te$_7$
Nazma Firdosh, Shreyashi Sinha, Indraneel Sinha, Mainpal Singh, Satyabrata Patnaik, Sujit Manna
https://arxiv.org/abs/2506.15540

Intertwined magnetic phase driven exchange bias and its impact on the anomalous Hall effect in MnBi$_4$Te$_7$
We report on the interplay between atomic scale inhomogeneity and competing magnetic phases and its effect on the anomalous Hall effect in the layered antiferromagnet MnBi$_4$Te$_7$, a natural superlattice hosting coexisting ferromagnetic and antiferromagnetic phases. Using a combination of scanning tunneling microscopy (STM), DC and AC magnetization, and magneto-transport measurements, we reveal that intrinsic Mn Bi antisite defects induce strong interlayer exchange coupling, giving rise to a …

Es wurde ja erwartet, aber tatsächlich fand ich den Punkt der Verstärkung bestehender Ungleichheiten bislang stark unterrepräsentiert in der Berichterstattung.
KI-Bias bei Gehaltsratschlägen: Wie Sprachmodelle Frauen systematisch benachteiligen - Notebookcheck.com News

Randall Munroe (of xkcd fame) posted about an analysis done by David R. Hagen related to his "missing 11th" comic. Great read, which includes some nice statistical analysis, but more interestingly, bias and artifacts.
https://bsky.app/profile/xkcd.com/post/3lrxgvl677k2w

“In the US, we have a set of electoral and legislative institutions that drive us toward only having two parties,”
Mark Copelovitch told us.
“That allowed the 20 to 25 percent of people that support the far right to basically take over one of the two parties,
and everything in our system is weighted towards the Republican constituency
— the Supreme Court, the gerrymandered House, the way the Senate is apportioned.
All of these things basically bias the electo…

“Neither our media nor our political system is designed to deal with a far right authoritarian party”
Mark Copelovitch unpacks his signature phrase.

High-Frequency First: A Two-Stage Approach for Improving Image INR
Sumit Kumar Dam, Mrityunjoy Gain, Eui-Nam Huh, Choong Seon Hong
https://arxiv.org/abs/2508.15582 https://

High-Frequency First: A Two-Stage Approach for Improving Image INR
Implicit Neural Representations (INRs) have emerged as a powerful alternative to traditional pixel-based formats by modeling images as continuous functions over spatial coordinates. A key challenge, however, lies in the spectral bias of neural networks, which tend to favor low-frequency components while struggling to capture high-frequency (HF) details such as sharp edges and fine textures. While prior approaches have addressed this limitation through architectural modifications or specialized …

Yield, noise and timing studies of ALICE ITS3 stitched sensor test structures: the MOST
Jory Sonneveld (on behalf of the ALICE collaboration), Ren\'e Barthel (on behalf of the ALICE collaboration), Szymon Bugiel (on behalf of the ALICE collaboration), Leonardo Cecconi (on behalf of the ALICE collaboration), Jo\~ao De Melo (on behalf of the ALICE collaboration), Martin Fransen (on behalf of the ALICE collaboration), Alessandro Grelli (on behalf of the ALICE collaboration), Isis Hobu…

Yield, noise and timing studies of ALICE ITS3 stitched sensor test structures: the MOST
In the LHC long shutdown 3, the ALICE experiment upgrades the inner layers of its Inner Tracker System with three layers of wafer-scale stitched sensors bent around the beam pipe. Two stitched sensor evaluation structures, the MOnolithic Stitched Sensor (MOSS) and MOnolithic Stitched Sensor with Timing (MOST) allow the study of yield dependence on circuit density, power supply segmentation, stitching demonstration for power and data transmission, performance dependence on reverse bias, charge c…

Leave No One Behind: Fairness-Aware Cross-Domain Recommender Systems for Non-Overlapping Users
Weixin Chen, Yuhan Zhao, Li Chen, Weike Pan
https://arxiv.org/abs/2507.17749 https…

Leave No One Behind: Fairness-Aware Cross-Domain Recommender Systems for Non-Overlapping Users
Cross-domain recommendation (CDR) methods predominantly leverage overlapping users to transfer knowledge from a source domain to a target domain. However, through empirical studies, we uncover a critical bias inherent in these approaches: while overlapping users experience significant enhancements in recommendation quality, non-overlapping users benefit minimally and even face performance degradation. This unfairness may erode user trust, and, consequently, negatively impact business engagement…

Ketanji Brown Jackson Wants to Save the Supreme Court From Itself
https://newrepublic.substack.com/p/supreme-court-epa-corporate-bias

Weighted Parameter Estimators of the Generalized Extreme Value Distribution in the Presence of Missing Observations
James H. McVittie, Orla A. Murphy
https://arxiv.org/abs/2506.15964

Weighted Parameter Estimators of the Generalized Extreme Value Distribution in the Presence of Missing Observations
Missing data occur in a variety of applications of extreme value analysis. In the block maxima approach to an extreme value analysis, missingness is often handled by either ignoring missing observations or dropping a block of observations from the analysis. However, in some cases, missingness may occur due to equipment failure during an extreme event, which can lead to bias in estimation. In this work, we propose weighted maximum likelihood and weighted moment-based estimators for the generaliz…

Kennedy's picks for vaccine advisory panel raise concerns about anti-vaccine bias (NBC News)
https://www.nbcnews.com/health/health-news/rfk-jr-advisory-panel-concerns-anti-vaccine-bias-rcna213496
http://www.memeorandum.com/250618/p125#a250618p125

Identifying Algorithmic and Domain-Specific Bias in Parliamentary Debate Summarisation
Eoghan Cunningham, James Cross, Derek Greene
https://arxiv.org/abs/2507.14221

Identifying Algorithmic and Domain-Specific Bias in Parliamentary Debate Summarisation
The automated summarisation of parliamentary debates using large language models (LLMs) offers a promising way to make complex legislative discourse more accessible to the public. However, such summaries must not only be accurate and concise but also equitably represent the views and contributions of all speakers. This paper explores the use of LLMs to summarise plenary debates from the European Parliament and investigates the algorithmic and representational biases that emerge in this context.…

Two-loop renormalization and running of galaxy bias
Thomas Bakx, Mathias Garny, Henrique Rubira, Zvonimir Vlah
https://arxiv.org/abs/2507.13905 https://

Two-loop renormalization and running of galaxy bias
We systematically extend the framework of galaxy bias renormalization to two-loop order. For the minimal complete basis of 29 deterministic bias operators up to fifth order in the density field and at leading order in gradient expansion we explicitly work out one- and two-loop renormalization. The latter is provided in terms of double-hard limits of bias kernels, which we find to depend on only one function of the ratio of the loop momenta. After including stochasticity in terms of composite op…

When Audio and Text Disagree: Revealing Text Bias in Large Audio-Language Models
Cheng Wang, Gelei Deng, Xianglin Yang, Han Qiu, Tianwei Zhang
https://arxiv.org/abs/2508.15407 h…

When Audio and Text Disagree: Revealing Text Bias in Large Audio-Language Models
Large Audio-Language Models (LALMs) are enhanced with audio perception capabilities, enabling them to effectively process and understand multimodal inputs that combine audio and text. However, their performance in handling conflicting information between audio and text modalities remains largely unexamined. This paper introduces MCR-BENCH, the first comprehensive benchmark specifically designed to evaluate how LALMs prioritize information when presented with inconsistent audio-text pairs. Throu…

Fairness for the People, by the People: Minority Collective Action
Omri Ben-Dov, Samira Samadi, Amartya Sanyal, Alexandru \c{T}ifrea
https://arxiv.org/abs/2508.15374 https://

Fairness for the People, by the People: Minority Collective Action
Machine learning models often preserve biases present in training data, leading to unfair treatment of certain minority groups. Despite an array of existing firm-side bias mitigation techniques, they typically incur utility costs and require organizational buy-in. Recognizing that many models rely on user-contributed data, end-users can induce fairness through the framework of Algorithmic Collective Action, where a coordinated minority group strategically relabels its own data to enhance fairne…

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

Thirteen newsroom leaders and ethicists discuss elements of journalistic ethics, including dealing with AI, anonymous leaks, bias, lies, and audience needs (Columbia Journalism Review)
https://www.cjr.org/feature/thirteen-journ

Thirteen Journalists on How They Are Rethinking Ethics 
We asked newsroom leaders and ethicists what they’re keeping or changing in an era of Trump, “fake news,” AI, and industry decline.

The Emperor's New Chain-of-Thought: Probing Reasoning Theater Bias in Large Reasoning Models
Qian Wang, Yubo Fan, Zhenheng Tang, Nuo Chen, Wenxuan Wang, Bingsheng He
https://arxiv.org/abs/2507.13758

The Emperor's New Chain-of-Thought: Probing Reasoning Theater Bias in Large Reasoning Models
Large Reasoning Models (LRMs) like DeepSeek-R1 and o1 are increasingly used as automated evaluators, raising critical questions about their vulnerability to the aesthetics of reasoning in LLM-as-a-judge settings. We introduce THEATER, a comprehensive benchmark to systematically evaluate this vulnerability-termed Reasoning Theater Bias (RTB)-by comparing LLMs and LRMs across subjective preference and objective factual datasets. Through investigation of six bias types including Simple Cues and Fa…

Who Gets the Mic? Investigating Gender Bias in the Speaker Assignment of a Speech-LLM
Dariia Puhach, Amir H. Payberah, \'Eva Sz\'ekely
https://arxiv.org/abs/2508.13603 h…

Who Gets the Mic? Investigating Gender Bias in the Speaker Assignment of a Speech-LLM
Similar to text-based Large Language Models (LLMs), Speech-LLMs exhibit emergent abilities and context awareness. However, whether these similarities extend to gender bias remains an open question. This study proposes a methodology leveraging speaker assignment as an analytic tool for bias investigation. Unlike text-based models, which encode gendered associations implicitly, Speech-LLMs must produce a gendered voice, making speaker selection an explicit bias cue. We evaluate Bark, a Text-to-Sp…

A Comprehensive Evaluation of the Sensitivity of Density-Ratio Estimation Based Fairness Measurement in Regression
Abdalwahab Almajed, Maryam Tabar, Peyman Najafirad
https://arxiv.org/abs/2508.14576

A Comprehensive Evaluation of the Sensitivity of Density-Ratio Estimation Based Fairness Measurement in Regression
The prevalence of algorithmic bias in Machine Learning (ML)-driven approaches has inspired growing research on measuring and mitigating bias in the ML domain. Accordingly, prior research studied how to measure fairness in regression which is a complex problem. In particular, recent research proposed to formulate it as a density-ratio estimation problem and relied on a Logistic Regression-driven probabilistic classifier-based approach to solve it. However, there are several other methods to esti…

KI-Update kompakt: Grok, Baustellenunfälle, Bias, vergiftete KI
Das "KI-Update" liefert werktäglich eine Zusammenfassung der wichtigsten KI-Entwicklungen.
https://www.hei…

Adversarial Hospital-Invariant Feature Learning for WSI Patch Classification
Mengliang Zhang, Jacob M. Luber
https://arxiv.org/abs/2508.14779 https://arxiv…

Adversarial Hospital-Invariant Feature Learning for WSI Patch Classification
Pathology foundation models (PFMs) have demonstrated remarkable potential in whole-slide image (WSI) diagnosis. However, pathology images from different hospitals often vary due to differences in scanning hardware and preprocessing styles, which may lead PFMs to inadvertently learn hospital-specific features, posing risks for clinical deployment. In this work, we present the first systematic study of domain bias in PFMs arising from hospital source characteristics. Specifically, we (1) construc…

Empirical likelihood meta analysis with publication bias correction under Copas-like selection model
Mengke Li, Yukun Liu, Pengfei Li, Jing Qin
https://arxiv.org/abs/2507.13615

Empirical likelihood meta analysis with publication bias correction under Copas-like selection model
Meta analysis is commonly-used to synthesize multiple results from individual studies. However, its validation is usually threatened by publication bias and between-study heterogeneity, which can be captured by the Copas selection model. Existing inference methods under this model are all based on conditional likelihood and may not be fully efficient. In this paper, we propose a full likelihood approach to meta analysis by integrating the conditional likelihood and a marginal semi-parametric em…

Assessing Trustworthiness of AI Training Dataset using Subjective Logic -- A Use Case on Bias
Koffi Ismael Ouattara, Ioannis Krontiris, Theo Dimitrakos, Frank Kargl
https://arxiv.org/abs/2508.13813

Assessing Trustworthiness of AI Training Dataset using Subjective Logic -- A Use Case on Bias
As AI systems increasingly rely on training data, assessing dataset trustworthiness has become critical, particularly for properties like fairness or bias that emerge at the dataset level. Prior work has used Subjective Logic to assess trustworthiness of individual data, but not to evaluate trustworthiness properties that emerge only at the level of the dataset as a whole. This paper introduces the first formal framework for assessing the trustworthiness of AI training datasets, enabling uncert…

Bias is a Math Problem, AI Bias is a Technical Problem: 10-year Literature Review of AI/LLM Bias Research Reveals Narrow [Gender-Centric] Conceptions of 'Bias', and Academia-Industry Gap
Sourojit Ghosh, Kyra Wilson
https://arxiv.org/abs/2508.11067

Bias is a Math Problem, AI Bias is a Technical Problem: 10-year Literature Review of AI/LLM Bias Research Reveals Narrow [Gender-Centric] Conceptions of 'Bias', and Academia-Industry Gap
The rapid development of AI tools and implementation of LLMs within downstream tasks has been paralleled by a surge in research exploring how the outputs of such AI/LLM systems embed biases, a research topic which was already being extensively explored before the era of ChatGPT. Given the high volume of research around the biases within the outputs of AI systems and LLMs, it is imperative to conduct systematic literature reviews to document throughlines within such research. In this paper, we c…

PC-Sampler: Position-Aware Calibration of Decoding Bias in Masked Diffusion Models
Pengcheng Huang, Shuhao Liu, Zhenghao Liu, Yukun Yan, Shuo Wang, Zulong Chen, Tong Xiao
https://arxiv.org/abs/2508.13021

PC-Sampler: Position-Aware Calibration of Decoding Bias in Masked Diffusion Models
Recent advances in masked diffusion models (MDMs) have established them as powerful non-autoregressive alternatives for sequence generation. Nevertheless, our preliminary experiments reveal that the generation quality of MDMs is still highly sensitive to the choice of decoding strategy. In particular, widely adopted uncertainty-based samplers suffer from two key limitations: a lack of global trajectory control and a pronounced bias toward trivial tokens in the early stages of decoding. These sh…

Trump-aligned legal group sues Michigan Law Review over 'Illegal DEI' bias (Kaelan Deese/Washington Examiner)
https://www.washingtonexaminer.com/news/3447681/trump-aligned-legal-group-sues-michigan-law-review/
http://www.memeorandum.com/250619/p93#a250619p93

Trumps anti-woke-Verordnung: Meta arbeitet mit rechtem Influencer
Um den vermeintlich woken Bias aus KI-Anwendungen zu bekommen, arbeitet Meta nun mit dem konservativen Robby Starbuck zusammen.
https://www.…

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 …

Where does non-Universality in Assembly Bias come from?
Charuhas Shiveshwarkar, Marilena Loverde, Christopher M. Hirata, Drew Jamieson
https://arxiv.org/abs/2508.11798 https://

Where does non-Universality in Assembly Bias come from?
Constraints on local primordial non-Gaussianity (LPnG) obtained from galaxy power spectra are limited by the perfect degeneracy between the LPnG parameter $f_{\rm NL}$ and the bias parameter $b_ϕ$ which encodes the response of galaxy clustering to a change in the amplitude of primordial curvature fluctuations. For galaxies observed by galaxy surveys, the relation between $b_ϕ$ and the galaxy bias $b_{g}$ is poorly understood and differs significantly from the universal mass function ansatz. I…

Sequential Confirmatory Factor Analysis: A Novel Approach to Latent Variable Measurement
Zachary Esses Johnson
https://arxiv.org/abs/2508.15611 https://arx…

Sequential Confirmatory Factor Analysis: A Novel Approach to Latent Variable Measurement
Factor score estimation in small sample sizes often encounters parameter bias and convergence failures when constructing hierarchical national/sub-national indices. This paper proposes a novel method for hierarchical factor analysis called "sequential Confirmatory Factor Analysis". Instead of estimating multiple levels of factors at the same time, this approach calculates factor scores sequentially from the lowest to highest levels. This sequential estimation keeps the original sample size in e…

Rich Lyons,
the University of California, Berkeley, chancellor,
challenged US House Republicanson Tuesday
as they questioned Lyons and leaders of Georgetown University and the City University of New York
in the latest hearing on antisemitism in higher education.
The committee accused the schools of failing to respond adequately to allegations of bias or discrimination,
however the university leaders said that disciplinary action had been taken where approp…

Large-dimensional Factor Analysis with Weighted PCA
Zhongyuan Lyu, Ming Yuan
https://arxiv.org/abs/2508.15675 https://arxiv.org/pdf/2508.15675

Large-dimensional Factor Analysis with Weighted PCA
Principal component analysis (PCA) is arguably the most widely used approach for large-dimensional factor analysis. While it is effective when the factors are sufficiently strong, it can be inconsistent when the factors are weak and/or the noise has complex dependence structure. We argue that the inconsistency often stems from bias and introduce a general approach to restore consistency. Specifically, we propose a general weighting scheme for PCA and show that with a suitable choice of weightin…

Exploring Gender Bias in Alzheimer's Disease Detection: Insights from Mandarin and Greek Speech Perception
Liu He, Yuanchao Li, Rui Feng, XinRan Han, Yin-Long Liu, Yuwei Yang, Zude Zhu, Jiahong Yuan
https://arxiv.org/abs/2507.12356

Exploring Gender Bias in Alzheimer's Disease Detection: Insights from Mandarin and Greek Speech Perception
Gender bias has been widely observed in speech perception tasks, influenced by the fundamental voicing differences between genders. This study reveals a gender bias in the perception of Alzheimer's Disease (AD) speech. In a perception experiment involving 16 Chinese listeners evaluating both Chinese and Greek speech, we identified that male speech was more frequently identified as AD, with this bias being particularly pronounced in Chinese speech. Acoustic analysis showed that shimmer values in…

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[2/5]:
- PC-Sampler: Position-Aware Calibration of Decoding Bias in Masked Diffusion Models
Pengcheng Huang, Shuhao Liu, Zhenghao Liu, Yukun Yan, Shuo Wang, Zulong Chen, Tong Xiao

BSI veröffentlicht Whitepaper zum Bias in der KI
Wenn sich KI-Systeme nicht so verhalten, wie erwünscht, stecken oft Verzerrungen in den Daten dahinter. Das BSI erklärt, wie man den Bias im System erkennt.
https://www.

Combating Homelessness Stigma with LLMs: A New Multi-Modal Dataset for Bias Detection
Jonathan A. Karr Jr., Benjamin F. Herbst, Ting Hua, Matthew Hauenstein, Georgina Curto, Nitesh V. Chawla
https://arxiv.org/abs/2508.13187

Combating Homelessness Stigma with LLMs: A New Multi-Modal Dataset for Bias Detection
Homelessness is a persistent social challenge, impacting millions worldwide. Over 770,000 people experienced homelessness in the U.S. in 2024. Social stigmatization is a significant barrier to alleviation, shifting public perception, and influencing policymaking. Given that online and city council discourse reflect and influence part of public opinion, it provides valuable insights to identify and track social biases. This research contributes to alleviating homelessness by acting on public opi…

Sycophancy under Pressure: Evaluating and Mitigating Sycophantic Bias via Adversarial Dialogues in Scientific QA
Kaiwei Zhang, Qi Jia, Zijian Chen, Wei Sun, Xiangyang Zhu, Chunyi Li, Dandan Zhu, Guangtao Zhai
https://arxiv.org/abs/2508.13743

Sycophancy under Pressure: Evaluating and Mitigating Sycophantic Bias via Adversarial Dialogues in Scientific QA
Large language models (LLMs), while increasingly used in domains requiring factual rigor, often display a troubling behavior: sycophancy, the tendency to align with user beliefs regardless of correctness. This tendency is reinforced by preference-based alignment techniques that optimize for user satisfaction but can undermine truthfulness. While relatively benign in casual dialogue, sycophancy poses serious risks in high-stakes settings such as scientific question answering (QA), where model ou…

Depth-Breadth Synergy in RLVR: Unlocking LLM Reasoning Gains with Adaptive Exploration
Zhicheng Yang, Zhijiang Guo, Yinya Huang, Yongxin Wang, Dongchun Xie, Yiwei Wang, Xiaodan Liang, Jing Tang
https://arxiv.org/abs/2508.13755

Depth-Breadth Synergy in RLVR: Unlocking LLM Reasoning Gains with Adaptive Exploration
Reinforcement Learning with Verifiable Reward (RLVR) has emerged as a powerful paradigm for unlocking reasoning capabilities in large language models, yet its full potential is hindered by two under-explored dimensions: Depth-the hardest problem a model can sample; Breadth-the number of instances consumed in a single iteration. We dissect the popular GRPO algorithm and reveal a systematic bias: the cumulative-advantage disproportionately weights samples with medium accuracy, while down-weightin…

Bias Delayed is Bias Denied? Assessing the Effect of Reporting Delays on Disparity Assessments
Jennah Gosciak (Cornell University), Aparna Balagopalan (Massachusetts Institute of Technology), Derek Ouyang (Stanford University), Allison Koenecke (Cornell University), Marzyeh Ghassemi (Massachusetts Institute of Technology), Daniel E. Ho (Stanford University)

Bias Delayed is Bias Denied? Assessing the Effect of Reporting Delays on Disparity Assessments
Conducting disparity assessments at regular time intervals is critical for surfacing potential biases in decision-making and improving outcomes across demographic groups. Because disparity assessments fundamentally depend on the availability of demographic information, their efficacy is limited by the availability and consistency of available demographic identifiers. While prior work has considered the impact of missing data on fairness, little attention has been paid to the role of delayed dem…

Social and Political Framing in Search Engine Results
Amrit Poudel, Tim Weninger
https://arxiv.org/abs/2507.13325 https://arxiv.org/p…

Social and Political Framing in Search Engine Results
Search engines play a crucial role in shaping public discourse by influencing how information is accessed and framed. While prior research has extensively examined various dimensions of search bias -- such as content prioritization, indexical bias, political polarization, and sources of bias -- an important question remains underexplored: how do search engines and ideologically-motivated user queries contribute to bias in search results. This study analyzes the outputs of major search engines u…

Replaced article(s) found for cs.CY. https://arxiv.org/list/cs.CY/new
[1/1]:
- Bias in Decision-Making for AI's Ethical Dilemmas: A Comparative Study of ChatGPT and Claude
Yile Yan, Yuqi Zhu, Wentao Xu

BIPOLAR: Polarization-based granular framework for LLM bias evaluation
Martin Pavl\'i\v{c}ek, Tom\'a\v{s} Filip, Petr Sos\'ik
https://arxiv.org/abs/2508.11061 https:…

BIPOLAR: Polarization-based granular framework for LLM bias evaluation
Large language models (LLMs) are known to exhibit biases in downstream tasks, especially when dealing with sensitive topics such as political discourse, gender identity, ethnic relations, or national stereotypes. Although significant progress has been made in bias detection and mitigation techniques, certain challenges remain underexplored. This study proposes a reusable, granular, and topic-agnostic framework to evaluate polarisation-related biases in LLM (both open-source and closed-source). …

Assessing the Reliability of LLMs Annotations in the Context of Demographic Bias and Model Explanation
Hadi Mohammadi, Tina Shahedi, Pablo Mosteiro, Massimo Poesio, Ayoub Bagheri, Anastasia Giachanou
https://arxiv.org/abs/2507.13138

Assessing the Reliability of LLMs Annotations in the Context of Demographic Bias and Model Explanation
Understanding the sources of variability in annotations is crucial for developing fair NLP systems, especially for tasks like sexism detection where demographic bias is a concern. This study investigates the extent to which annotator demographic features influence labeling decisions compared to text content. Using a Generalized Linear Mixed Model, we quantify this inf luence, finding that while statistically present, demographic factors account for a minor fraction ( 8%) of the observed varianc…

Inductive Bias Extraction and Matching for LLM Prompts
Christian M. Angel, Francis Ferraro
https://arxiv.org/abs/2508.10295 https://arxiv.org/pdf/2508.1029…

Inductive Bias Extraction and Matching for LLM Prompts
The active research topic of prompt engineering makes it evident that LLMs are sensitive to small changes in prompt wording. A portion of this can be ascribed to the inductive bias that is present in the LLM. By using an LLM's output as a portion of its prompt, we can more easily create satisfactory wording for prompts. This has the effect of creating a prompt that matches the inductive bias in model. Empirically, we show that using this Inductive Bias Extraction and Matching strategy improves …

ALIGN: Word Association Learning for Cross-Cultural Generalization in Large Language Models
Chunhua Liu, Kabir Manandhar Shrestha, Sukai Huang
https://arxiv.org/abs/2508.13426 h…

ALIGN: Word Association Learning for Cross-Cultural Generalization in Large Language Models
As large language models (LLMs) increasingly mediate cross-cultural communication, their behavior still reflects the distributional bias of the languages and viewpoints that are over-represented in their pre-training corpora. Yet, it remains a challenge to model and align culture due to limited cultural knowledge and a lack of exploration into effective learning approaches. We introduce a cost-efficient, cognitively grounded remedy: parameter-efficient fine-tuning on native speakers' free word-…