Tootfinder

White House launches new 'media bias' tracker on site (Ashleigh Fields/NewsNation)
https://www.newsnationnow.com/politics/white-house-launches-new-media-bias-tracker-on-site/
http://www.memeorandum.com/251129/p51#a251129p51

Investigating Language and Retrieval Bias in Multilingual Previously Fact-Checked Claim Detection
Ivan Vykopal, Antonia Karamolegkou, Jaroslav Kop\v{c}an, Qiwei Peng, Tom\'a\v{s} Jav\r{u}rek, Michal Gregor, Mari\'an \v{S}imko
https://arxiv.org/abs/2509.25138

Investigating Language and Retrieval Bias in Multilingual Previously Fact-Checked Claim Detection
Multilingual Large Language Models (LLMs) offer powerful capabilities for cross-lingual fact-checking. However, these models often exhibit language bias, performing disproportionately better on high-resource languages such as English than on low-resource counterparts. We also present and inspect a novel concept - retrieval bias, when information retrieval systems tend to favor certain information over others, leaving the retrieval process skewed. In this paper, we study language and retrieval b…

The White House launches a media bias tracker on its site for news reports the president disagrees with, citing outlets like The Washington Post, CNN, and more (Scott Nover/Washington Post)
https://www.washingtonpost.com/business/2025/11/28/trump-media-criticism/

Trump ramps up reporter attacks with White House media bias tracker
A new White House webpage presents a “Hall of Shame” for news reports that the president disagrees with, coming after Trump voiced personal attacks on female reporters at ABC, CBS, the New York Times and Bloomberg.

LLM Watermark Evasion via Bias Inversion
Jeongyeon Hwang, Sangdon Park, Jungseul Ok
https://arxiv.org/abs/2509.23019 https://arxiv.org/pdf/2509.23019

LLM Watermark Evasion via Bias Inversion
Watermarking for large language models (LLMs) embeds a statistical signal during generation to enable detection of model-produced text. While watermarking has proven effective in benign settings, its robustness under adversarial evasion remains contested. To advance a rigorous understanding and evaluation of such vulnerabilities, we propose the \emph{Bias-Inversion Rewriting Attack} (BIRA), which is theoretically motivated and model-agnostic. BIRA weakens the watermark signal by suppressing the…

Here is a White House website falsely asserting that military personnel CANNOT refuse illegal orders
And falsely accusing Democrats of asserting Trump has issued illegal orders -- in a video which doesn't mention Trump
The felon doth protest too much
https://www.whitehouse.gov/mediabias/

Media Offenders
Uncover Media Bias & Hold Fake News Accountable. False claims debunked, serial offenders immortalized in the Hall of Fame, networks ranked on the Leaderboard of Repeat Lies. Stay informed, stay accurate.

Color, Gender, and Bias: Examining the Role of Stereotyped Colors in Visualization-Driven Pay Decisions
Florent Cabric, Margret Vilborg Bjarnadottir, Petra Isenberg
https://arxiv.org/abs/2509.24999

Color, Gender, and Bias: Examining the Role of Stereotyped Colors in Visualization-Driven Pay Decisions
We investigate the impact of stereotyped gender-color associations in a visualization-driven decision-making task. In the context of gender data visualization, the well-known "pink for girls and blue for boys" color assignment is associated with stereotypes that could bias readers and decision-makers. Understanding the effects of using stereotyped colors in visualizations for decision-making can help designers better choose colors in stereotype-prone contexts. We therefore explore the potential…

When Greedy Wins: Emergent Exploitation Bias in Meta-Bandit LLM Training
Sanxing Chen, Xiaoyin Chen, Yukun Huang, Roy Xie, Bhuwan Dhingra
https://arxiv.org/abs/2509.24923 https:…

When Greedy Wins: Emergent Exploitation Bias in Meta-Bandit LLM Training
While Large Language Models (LLMs) hold promise to become autonomous agents, they often explore suboptimally in sequential decision-making. Recent work has sought to enhance this capability via supervised fine-tuning (SFT) or reinforcement learning (RL), improving regret on the classic multi-armed bandit task. However, it remains unclear how these learning methods shape exploration strategies and how well they generalize. We investigate both paradigms by training LLMs with SFT on expert traject…

Könnte confirmation bias sein, aber irgendwie häuft sich bei mir der Eindruck, dass Software schlechter wird. Excel ist langsamer als früher, wenn es um große Tabellen oder Autovervollständigung geht. Die App der Badischen Zeitung muss häufiger neu gestartet werden. Pixums Fotobuch-Software vergisst regelmäßig die Einstellung für das Grid. Usw.

Achieving Fair Skin Lesion Detection through Skin Tone Normalization and Channel Pruning
Zihan Wei, Tapabrata Chakraborti
https://arxiv.org/abs/2509.22712 https://

Achieving Fair Skin Lesion Detection through Skin Tone Normalization and Channel Pruning
Recent works have shown that deep learning based skin lesion image classification models trained on unbalanced dataset can exhibit bias toward protected demographic attributes such as race, age,and gender. Current bias mitigation methods usually either achieve high level of fairness with the degradation of accuracy, or only improve the model fairness on a single attribute. Additionally usually most bias mitigation strategies are either pre hoc through data processing or post hoc through fairnes…

Inductive Bias and Spectral Properties of Single-Head Attention in High Dimensions
Fabrizio Boncoraglio, Vittorio Erba, Emanuele Troiani, Florent Krzakala, Lenka Zdeborov\'a
https://arxiv.org/abs/2509.24914

Inductive Bias and Spectral Properties of Single-Head Attention in High Dimensions
We study empirical risk minimization in a single-head tied-attention layer trained on synthetic high-dimensional sequence tasks, given by the recently introduced attention-indexed model. Using tools from random matrix theory, spin-glass physics, and approximate message passing, we derive sharp asymptotics for training and test errors, locate interpolation and recovery thresholds, and characterize the limiting spectral distribution of the learned weights. Weight decay induces an implicit nuclear…

Feedback promotes efficient-coding while attenuating bias in recurrent neural networks
Holly Kular, Robert Kim, John Serences, Nuttida Rungratsameetaweemana
https://arxiv.org/abs/2509.23104

Feedback promotes efficient-coding while attenuating bias in recurrent neural networks
Studies of human decision-making demonstrate that environmental regularities, such as natural image statistics or intentionally nonuniform stimulus probabilities, can be exploited to improve efficiency (termed `efficient-coding'). Conversely, from a machine learning perspective, such nonuniform stimulus properties can lead to biased neural networks with poor generalization performance. Understanding how the brain flexibly leverages stimulus bias while maintaining robust generalization could lea…

Anti-Regulatory AI: How "AI Safety" is Leveraged Against Regulatory Oversight
Rui-Jie Yew, Brian Judge
https://arxiv.org/abs/2509.22872 https://a…

Anti-Regulatory AI: How "AI Safety" is Leveraged Against Regulatory Oversight
AI companies increasingly develop and deploy privacy-enhancing technologies, bias-constraining measures, evaluation frameworks, and alignment techniques -- framing them as addressing concerns related to data privacy, algorithmic fairness, and AI safety. This paper examines the ulterior function of these technologies as mechanisms of legal influence. First, we examine how encryption, federated learning, and synthetic data -- presented as enhancing privacy and reducing bias -- can operate as mech…

Non-Altermagnetic Origin of Exchange Bias Behaviors in Incoherent RuO$_2$/Fe Bilayer Heterostructures
Shelby S. Fields, Joseph C. Prestigiacomo, Cory D. Cress, Nicholas G. Combs, Olaf van 't Erve, Patrick G. Callahan, Keith E. Knipling, Michelle E. Jamer, Frank M. Abel, Feng Ye, Arianna Minelli, Zachary J. Morgan, Haile Ambaye, Masaaki Matsuda, Avishek Maity, Valeria Lauter, Steven P. Bennett

Non-Altermagnetic Origin of Exchange Bias Behaviors in Incoherent RuO$_2$/Fe Bilayer Heterostructures
Initially identified as a promising altermagnetic (AM) candidate, rutile RuO$_2$ has since become embroiled in controversy due to contradictory findings of modeling and measurements of the magnetic properties of bulk crystals and thin films. For example, despite observations of a bulk non-magnetic state using density functional theory, neutron scattering, and muon spin resonance measurements, patterned RuO$_2$ Hall bars and film heterostructures display magnetotransport signatures of magnetic o…

Noise mitigation in quantum enhanced fiber optic gyroscopes
Stefan Evans, Joanna Ptasinski
https://arxiv.org/abs/2509.25159 https://arxiv.org/pdf/2509.2515…

Noise mitigation in quantum enhanced fiber optic gyroscopes
We analyze noise sources in a quantum-enhanced fiber optic gyroscope (FOG), aiming toward improving the feasibility of long (multiple km) fiber lengths and higher order $N>2$ polarization entangled N00N states. We focus on one of the leading sources of quantum FOG phase uncertainty, uncorrelated photon saturation. We characterize the optimal ranges of phase bias angles which minimize this uncertainty to allow for sub-shot noise precision. As an example, we apply the present-day leading quantum …

United We Stand: Towards End-to-End Log-based Fault Diagnosis via Interactive Multi-Task Learning
Minghua He, Chiming Duan, Pei Xiao, Tong Jia, Siyu Yu, Lingzhe Zhang, Weijie Hong, Jin Han, Yifan Wu, Ying Li, Gang Huang
https://arxiv.org/abs/2509.24364

United We Stand: Towards End-to-End Log-based Fault Diagnosis via Interactive Multi-Task Learning
Log-based fault diagnosis is essential for maintaining software system availability. However, existing fault diagnosis methods are built using a task-independent manner, which fails to bridge the gap between anomaly detection and root cause localization in terms of data form and diagnostic objectives, resulting in three major issues: 1) Diagnostic bias accumulates in the system; 2) System deployment relies on expensive monitoring data; 3) The collaborative relationship between diagnostic tasks …

An Augmented Lagrangian Value Function Method for Lower-level Constrained Stochastic Bilevel Optimization
Hantao Nie, Jiaxiang Li, Zaiwen Wen
https://arxiv.org/abs/2509.24249 ht…

An Augmented Lagrangian Value Function Method for Lower-level Constrained Stochastic Bilevel Optimization
Recently, lower-level constrained bilevel optimization has attracted increasing attention. However, existing methods mostly focus on either deterministic cases or problems with linear constraints. The main challenge in stochastic cases with general constraints is the bias and variance of the hyper-gradient, arising from the inexact solution of the lower-level problem. In this paper, we propose a novel stochastic augmented Lagrangian value function method for solving stochastic bilevel optimizat…

Trump ramps up reporter attacks with White House media bias tracker (Scott Nover/Washington Post)
https://www.washingtonpost.com/business/2025/11/28/trump-media-criticism/
http://www.memeorandum.com/251129/p46#a251129p46

A Greedy PDE Router for Blending Neural Operators and Classical Methods
Sahana Rayan, Yash Patel, Ambuj Tewari
https://arxiv.org/abs/2509.24814 https://arx…

A Greedy PDE Router for Blending Neural Operators and Classical Methods
When solving PDEs, classical numerical solvers are often computationally expensive, while machine learning methods can suffer from spectral bias, failing to capture high-frequency components. Designing an optimal hybrid iterative solver--where, at each iteration, a solver is selected from an ensemble of solvers to leverage their complementary strengths--poses a challenging combinatorial problem. While the greedy selection strategy is desirable for its constant-factor approximation guarantee to …

An SoS Entropy Dichotomy via Windowed Hypercontractivity
Marko Lela
https://arxiv.org/abs/2509.24280 https://arxiv.org/pdf/2509.24280

An SoS Entropy Dichotomy via Windowed Hypercontractivity
We prove an entropy versus degree dichotomy for low-degree tests and the Sum-of-Squares (SoS) hierarchy on a calibrated window after a gadget layer. For a target distribution \(μ\) and a product-like proxy \(u\), we study the low-degree discrepancy \(Δ_k(μ,u)\), defined as the optimal distinguishing advantage of degree \(\le k\) polynomial tests. Using a bias-orthonormal Walsh basis and a test-moment equivalence on the window, we relate \(Δ_k\) (up to constants) to the squared \(\ell_2\) ma…

Energy loss of heavy-flavor quarks in color string medium
Daria Prokhorova, Shuzhe Shi, Evgeny Andronov
https://arxiv.org/abs/2509.24316 https://arxiv.org/…

Energy loss of heavy-flavor quarks in color string medium
The paper presents preliminary estimates of heavy-flavor (HF) quark energy loss during its propagation through the non-equilibrated medium formed in minimum bias proton-proton (p+p) collisions at LHC energies. The study is inspired by the ongoing hot debates on whether tiny droplets of Quark-Gluon Plasma can be created in collisions of small systems. In this work, we model a p+p event with a fluctuating number of color strings originated from multi-pomeron exchanges. Considered longitudinal osc…

Online Specific Emitter Identification via Collision-Alleviated Signal Hash
Hongyu Wang, Wenjia Xu, Guangzuo Li, Siyuan Wan, Yaohua Sun, Jiuniu Wang, Mugen Peng
https://arxiv.org/abs/2509.23807

Online Specific Emitter Identification via Collision-Alleviated Signal Hash
Specific Emitter Identification (SEI) has been widely studied, aiming to distinguish signals from different emitters given training samples from those emitters. However, real-world scenarios often require identifying signals from novel emitters previously unseen. Since these novel emitters only have a few or no prior samples, existing models struggle to identify signals from novel emitters online and tend to bias toward the distribution of seen emitters. To address these challenges, we propose …

Technique-agnostic exoplanet demography for the Roman era - I. Testing a retrieval framework using simulated Kepler-like transit datasets
Akshay Priyadarshi, Eamonn Kerins
https://arxiv.org/abs/2509.24401

Technique-agnostic exoplanet demography for the Roman era - I. Testing a retrieval framework using simulated Kepler-like transit datasets
The Nancy Grace Roman Space Telescope (Roman) will unveil for the first time the full architecture of planetary systems across Galactic distances through the discovery of up to 200,000 cool and hot exoplanets using microlensing and transit detection methods. Roman's huge exoplanet haul, and Galactic reach, will require new methods to leverage the full exoplanet demographic content of the combined microlensing and transit samples, given the different sensitivity bias of the techniques to planet …

Closing the Evidence Gap: reddemcee, a Fast Adaptive Parallel Tempering Sampler
Pablo A. Pe\~na R., James S. Jenkins
https://arxiv.org/abs/2509.24870 https://

Closing the Evidence Gap: reddemcee, a Fast Adaptive Parallel Tempering Sampler
Markov Chain Monte Carlo (MCMC) excels at sampling complex posteriors but traditionally lags behind nested sampling in accurate evidence estimation, which is crucial for model comparison in astrophysical problems. We introduce reddemcee, an Adaptive Parallel Tempering Ensemble Sampler, aiming to close this gap by simultaneously presenting next-generation automated temperature-ladder adaptation techniques and robust, low-bias evidence estimators. reddemcee couples an affine-invariant stretch mov…

Nonparametric and Semiparametric Estimation of Upward Rank Mobility Curves
Tsung-Chih Lai, Jia-Han Shih, Yi-Hau Chen
https://arxiv.org/abs/2509.23174 https://

Nonparametric and Semiparametric Estimation of Upward Rank Mobility Curves
We introduce the upward rank mobility curve as a new measure of intergenerational mobility that captures upward movements across the entire parental income distribution. Our approach extends Bhattacharya and Mazumder (2011) by conditioning on a single parental income rank, thereby eliminating aggregation bias. We show that the measure can be characterized solely by the copula of parent and child income, and we propose a nonparametric copula-based estimator with better properties than kernel-bas…

Tunneling spectroscopy of two-dimensional superconductors with the quantum twisting microscope
Nemin Wei, Felix von Oppen, Leonid I. Glazman
https://arxiv.org/abs/2509.22902 htt…

Tunneling spectroscopy of two-dimensional superconductors with the quantum twisting microscope
The ongoing discoveries of graphene-based superconductors underscore the quest to understand the structure of new superconducting orders. We develop a theory that facilitates the use of the quantum twisting microscope (QTM) for that purpose. This work investigates momentum-conserving tunneling across a planar junction formed by a normal monolayer graphene tip and a superconducting graphene sample within the QTM setting. We show that the bias dependence of the zero-temperature tunneling conducta…

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

Anthropic open sources a method to score AI model political evenhandedness; Gemini 2.5 Pro got 97%, Grok 4 96%, Claude Opus 4.1 95%, GPT-5 89%, and Llama 4 66% (Ina Fried/Axios)
https://www.axios.com/2025/11/13/anthropic-bot-bias-data

Anthropic tries to measure bot bias
There's no consensus over what constitutes political bias in AI systems.

Magnetic-field dependent vortex dynamics and critical currents in superconducting microwires with regular large-area perforation by pinholes
Dong Zhu, Ilya Charaev, Konstantin Ilin, Andreas Schilling
https://arxiv.org/abs/2509.23404

Magnetic-field dependent vortex dynamics and critical currents in superconducting microwires with regular large-area perforation by pinholes
We report on results of simulations and experiments of vortex states in superconducting micro-wires with periodic rectangular pinhole structures. The simulations have been performed by means of numerically solving the time-dependent Ginzburg-Landau (TDGL) equations. With increasing bias current and for different values of the external magnetic field applied normal to the structure plane, we observe at first a vortex free Meissner state, followed by a resistive vortex-flow mixed state and a stat…

Analyzing and Evaluating Unbiased Language Model Watermark
Yihan Wu, Xuehao Cui, Ruibo Chen, Heng Huang
https://arxiv.org/abs/2509.24048 https://arxiv.org/…

Analyzing and Evaluating Unbiased Language Model Watermark
Verifying the authenticity of AI-generated text has become increasingly important with the rapid advancement of large language models, and unbiased watermarking has emerged as a promising approach due to its ability to preserve output distribution without degrading quality. However, recent work reveals that unbiased watermarks can accumulate distributional bias over multiple generations and that existing robustness evaluations are inconsistent across studies. To address these issues, we introdu…

Beyond Western Politics: Cross-Cultural Benchmarks for Evaluating Partisan Associations in LLMs
Divyanshu Kumar, Ishita Gupta, Nitin Aravind Birur, Tanay Baswa, Sahil Agarwal, Prashanth Harshangi
https://arxiv.org/abs/2509.22711

Beyond Western Politics: Cross-Cultural Benchmarks for Evaluating Partisan Associations in LLMs
Partisan bias in LLMs has been evaluated to assess political leanings, typically through a broad lens and largely in Western contexts. We move beyond identifying general leanings to examine harmful, adversarial representational associations around political leaders and parties. To do so, we create datasets \textit{NeutQA-440} (non-adversarial prompts) and \textit{AdverQA-440} (adversarial prompts), which probe models for comparative plausibility judgments across the USA and India. Results show …

Code-switching Speech Recognition Under the Lens: Model- and Data-Centric Perspectives
Hexin Liu, Haoyang Zhang, Qiquan Zhang, Xiangyu Zhang, Dongyuan Shi, Eng Siong Chng, Haizhou Li
https://arxiv.org/abs/2509.24310

Code-switching Speech Recognition Under the Lens: Model- and Data-Centric Perspectives
Code-switching automatic speech recognition (CS-ASR) presents unique challenges due to language confusion introduced by spontaneous intra-sentence switching and accent bias that blurs the phonetic boundaries. Although the constituent languages may be individually high-resource, the scarcity of annotated code-switching data further compounds these challenges. In this paper, we systematically analyze CS-ASR from both model-centric and data-centric perspectives. By comparing state-of-the-art algor…

Writing unit tests for my random number generation library continues to be difficult. My tests are failing because the bias in the distribution exceeds my expectations, but I'm wondering whether I should just repeat the test more times and permit it to exceed expectations some of the time (as long as it does it symmetrically/rarely/etc. My gut tells me that second-order expectations aren't any better than first-order expectations, but another part of me disagrees.
Thinking more as I write this (writing is thinking): second-order tests can at least give me better info to work with towards fixing things I think! So maybe I'll invest in them.
#coding

#SteadySupporter
Wie neutral sind KI-Systeme bei der medizinischen Diagnostik?
Eine neue Studie zeigt, dass selbst auf Expertenniveau arbeitende Systeme #Krankheiten übersehen und dabei marginalisierte Gruppen benachteiligen können.

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 is a good example of survivor bias:
https://www.thetimes.com/life-style/health-fitness/article/david-cameron-interview-prostate-cancer-kmtc2zj78

Source: the BBC plans to expand its editorial standards committee, diluting conservative board member Robbie Gibb's influence, and create a deputy DG position (Michael Savage/The Guardian)
https://www.theguardian.com/media/2025/nov

A profile of Wikipedia co-founder Larry Sanger, who left in 2002 and whose claims that Wikipedia has a liberal bias are fueling the right's campaign against it (Will Oremus/Washington Post)

A Wikipedia cofounder is fueling the right’s campaign against it
Larry Sanger’s claims of a liberal slant and mismanagement at the world’s dominant online encyclopedia are being enthusiastically embraced on the right.

Learning What To Hear: Boosting Sound-Source Association For Robust Audiovisual Instance Segmentation
Jinbae Seo, Hyeongjun Kwon, Kwonyoung Kim, Jiyoung Lee, Kwanghoon Sohn
https://arxiv.org/abs/2509.22740

Learning What To Hear: Boosting Sound-Source Association For Robust Audiovisual Instance Segmentation
Audiovisual instance segmentation (AVIS) requires accurately localizing and tracking sounding objects throughout video sequences. Existing methods suffer from visual bias stemming from two fundamental issues: uniform additive fusion prevents queries from specializing to different sound sources, while visual-only training objectives allow queries to converge to arbitrary salient objects. We propose Audio-Centric Query Generation using cross-attention, enabling each query to selectively attend to…

As beautiful as a sailboat looks. In my eyes it's totally not worth the effort to own one (on lakes in Germany). While I enjoy helping my friends with their boats occasionally ... the amount of maintenance just confirms my bias every time :D

@… @… yeah, I always feel awkward boosting things that might give them an 11ty bias but someone in the community might want to pitch in!

I like to play around as an anonymous commenter in online newspaper columns. If you point out the biases of #AI systems, the comment gets deleted because it is considered too polemical.
The comment was addressing an article about AI in public service and the use in refugee applications 🫣

tdor

What is bias but the broken autoimmune response of cisheteropatriarchy?

The toy aisle is still full of gender bias. Here's how to navigate it these holidays
https://phys.org/news/2025-12-toy-aisle-full-gender-bias.html

Alarming findings were published in the journal "Cell Reports Medicine",
showing that four leading
AI-enhanced pathology diagnostic systems
differ in accuracy depending on patients‘ age, gender, and race
— demographic data that the AI is extracting directly from pathology slides ... a feat that’s impossible for human doctors.

Doctors Catch Cancer-Diagnosing AI Extracting Patients’ Race Data and Being Racist With It
A new study reveals an alarming bias present in four leading AI cancer screening tools, related to race, gender, and age.

Remote coding interviews where you have to use your own machine have so much implicit bias built into them. As I haven't had a personal OS X machine in a decade, I have to use my Linux box which leads to...interesting shenanigans.

And we need to get clear about this, because LLMs •do• embed bias and their designers •can• steer and shape that bias and people •will• and •are• using that to shape public thought. Closed-source LLMs are an oligarch’s wet dream of thought control. And not all the oligarchs are as clueless as Musk.
We need to get clear on what kinds of evidence show bias — and bias by design — in LLMs. We need to train reporters in how to report this. We need to get our tools sharp. Like…yesterday.
/end

A profile of Wikipedia co-founder Larry Sanger, who left in 2002 and whose claims that Wikipedia has a liberal bias are fueling the right's campaign against it (Will Oremus/Washington Post)

A Wikipedia cofounder is fueling the right’s campaign against it
Larry Sanger’s claims of a liberal slant and mismanagement at the world’s dominant online encyclopedia are being enthusiastically embraced on the right.

Glad to hear the Director of the BBC is resigning in disgrace at their utter failure to ensure a 2026 series of Doctor Who. Any DG that can't even arrange for ten episodes of Doctor Who is clearly incompetent and unsuited to the...
Wait? It was nothing to do with Doctor Who? It was about Political Bias?
They think he was too left wing?
Hahhhahaa. Hahhaa. 🤣 😆
Too left wing.
😆
Stop it, you're killing me.
🤣
#bbc #bias

I love Cosmology. Nothing is more worthless than last year's textbook. Especially on the basics. It keeps the field humble.
Strong progenitor age bias in supernova cosmology – II. Alignment with DESI BAO and signs of a non-accelerating universe | Monthly Notices of the Royal Astronomical Society | Oxford Academic
https://academic.oup.com/mnras/article/544/1/975/8281988

Strong progenitor age bias in supernova #cosmology – I. Robust and ubiquitous evidence from a larger sample of host galaxies in a broader redshift range / II. Alignment with DESI BAO and signs of a non-accelerating universe: https://academic.oup.com/mnras/article/538/4/3340/8098234 / https://academic.oup.com/mnras/article/544/1/975/8281988 -> Universe's expansion 'is now slowing, not speeding up': https://ras.ac.uk/news-and-press/research-highlights/universes-expansion-now-slowing-not-speeding (this has been claimed by the group since at least 2023 - http://vietnam.in2p3.fr/2023/windows/transparencies/02_tuesday/Astro_5/01_Son.pdf - and is rejected by the SN cosmology community as https://bsky.app/profile/astromarc.bsky.social/post/3m4xmikwfds26 explains).

https://www.theatlantic.com/ideas/2025/11/right-wing-attack-wikipedia-bias-musk-cruz/684886/?utm_source=feed

The Right-Wing Attack on Wikipedia
The free internet encyclopedia is widely used to train AI. That’s why conservatives are trying to dethrone it.

But in all seriousness, take it with a pinch of salt and check with the vendor directly if you really care. Checking this stuff is hard.
Honestly, at best I can really only say, "I *think* the others have not updated but I may be wrong."
Also expect bias when I talk about competitors [and expect the same from representatives of other browsers].

Evaluating and Mitigating LLM-as-a-judge Bias in Communication Systems
Jiaxin Gao, Chen Chen, Yanwen Jia, Xueluan Gong, Kwok-Yan Lam, Qian Wang
https://arxiv.org/abs/2510.12462 …

Evaluating and Mitigating LLM-as-a-judge Bias in Communication Systems
Large Language Models (LLMs) are increasingly being used to autonomously evaluate the quality of content in communication systems, e.g., to assess responses in telecom customer support chatbots. However, the impartiality of these AI "judges" is not guaranteed, and any biases in their evaluation criteria could skew outcomes and undermine user trust. In this paper, we systematically investigate judgment biases in two LLM-as-a-judge models (i.e., GPT-Judge and JudgeLM) under the point-wise scoring…

RE: https://neuromatch.social/@MolemanPeter/115604657740821359
Survivor bias in this case is: only men that survived after screening report this and not the men that died despite the screening (sorry for the rather cynical way of wording). In additi…

"A new investigation of Elon #Musk’s X by Sky News found that every account set up by reporters, 'no matter their political orientation, was fed a glut of rightwing content', much of which was extreme. The experts it consulted believe this pattern could have resulted only from an algorithm engineered for this purpose, and that 'an algorithmic bias must be decided by senior people at …

The Oatmeal comic about "AI" "art" is great, but I have to critize the "I like AI, I use AI" part; there's a selection bias at work—he sees the generated "art" as very obviously for what it is, (dehumanizing low-effort slop) but doesn't make the logical conclusion that this is true for _any type of output_ (e.g. text); it always dehumanizing low-effort slop that's not worth looking at, listening to or reading.

"I do not want to sound like a tin-foil hat conspiracy theorist, but your country, your neighbourhood, your job…your entire life is already controlled by artificial intelligence. If you do not like the bit in Star Trek: Discovery where we find that Control has taken over Starfleet, then I have bad news: an AI is running the military, too."

Artificial Intelligence, Bias, And Opportunity
I do not want to sound like a tin-foil hat conspiracy theorist, but your country, your neighbourhood, your job…your entire life is already controlled by artificial intelligence. If you do not like the bit in Star Trek: Discovery where we find that Control has taken over Starfleet, then I have bad news: an AI is running the military, too.

Wikipedia’s rejoinder to Ted Cruz is entirely fair and accurate, but don’t kid yourself that Cruz is interested in fairness or accuracy, he’s just pushing back against the tendency of factual reporting to exhibit a liberal bias.
#wikipedia

Ted Cruz doesn’t seem to understand Wikipedia, lawyer for Wikimedia says
Wikipedia host’s lawyer wants to help Ted Cruz understand how the platform works.

The Social Cost of Intelligence: Emergence, Propagation, and Amplification of Stereotypical Bias in Multi-Agent Systems
Thi-Nhung Nguyen, Linhao Luo, Thuy-Trang Vu, Dinh Phung
https://arxiv.org/abs/2510.10943

The Social Cost of Intelligence: Emergence, Propagation, and Amplification of Stereotypical Bias in Multi-Agent Systems
Bias in large language models (LLMs) remains a persistent challenge, manifesting in stereotyping and unfair treatment across social groups. While prior research has primarily focused on individual models, the rise of multi-agent systems (MAS), where multiple LLMs collaborate and communicate, introduces new and largely unexplored dynamics in bias emergence and propagation. In this work, we present a comprehensive study of stereotypical bias in MAS, examining how internal specialization, underlyi…

Two new NERDS papers: Algorithmic bias, street network simplification
https://nerds.itu.dk/2025/11/03/two-new-nerds-papers-algorithmic-bias-street-network-simplification/

Days after immigration officials announced the death of a detainee at Newark migrant jail Delaney Hall,
Rep. LaMonica McIver visited the facility for the first time since prosecutors allege she assaulted federal agents there during a melee in May.
McIver’s visit coincided with a new filing in the Newark Democrat’s criminal case
in which her attorneys accuse prosecutors of failing to produce key evidence that they say shows bias among the agents involved in the scuffle,

Oversight visit brings Rep. McIver back to migrant jail at center of her criminal case - BLKNewsNow.com
NEWARK — Days after immigration officials announced the death of a detainee at Newark migrant jail Delaney Hall, Rep. LaMonica McIver visited the facility for the first time since prosecutors allege she assaulted federal agents there during a melee in May.

"The Telegraph’s Record of Climate Falsehoods"
#News #Climate #ClimateChange

The Telegraph’s Record of Climate Falsehoods
The Telegraph, which has accused the BBC of bias and a lack of editorial rigour, has been forced to amend a swathe of climate inaccuracies. The BBC’s director-general and CEO resigned this weekend after a critical review of the broadcaster’s coverage was leaked to The Telegraph. The Telegraph has used this opportunity to slam the BBC […]

AI is Dunning-Kruger as a service
«A cognitive bias, where people with little expertise or ability assume they have superior expertise or ability. This overestimation occurs as a result of the fact that they don’t have enough knowledge to know they don’t have enough knowledge.»
🤯 https://…

BBC board member Shumeet Banerji resigns, saying he was cut out of discussions that led to the resignation of the BBC's director general, Tim Davie (Michael Savage/The Guardian)
https://www.theguardian.com/media/2025/nov

Analysing Moral Bias in Finetuned LLMs through Mechanistic Interpretability
Bianca Raimondi, Daniela Dalbagno, Maurizio Gabbrielli
https://arxiv.org/abs/2510.12229 https://

Analysing Moral Bias in Finetuned LLMs through Mechanistic Interpretability
Large language models (LLMs) have been shown to internalize human-like biases during finetuning, yet the mechanisms by which these biases manifest remain unclear. In this work, we investigated whether the well-known Knobe effect, a moral bias in intentionality judgements, emerges in finetuned LLMs and whether it can be traced back to specific components of the model. We conducted a Layer-Patching analysis across 3 open-weights LLMs and demonstrated that the bias is not only learned during finet…

'Partisan Bias' is Like 'Cancer'
Alec Ramsay
https://arxiv.org/abs/2510.05114 https://arxiv.org/pdf/2510.05114

'Partisan Bias' is Like 'Cancer'
The colloquial phrase "partisan bias" encompasses multiple distinct conceptions of bias, including partisan advantage, packing & cracking, and partisan symmetry. All are useful and have their place, and there are several proposed measures of each. While different measures frequently signal the direction of bias consistently for redistricting plans, sometimes the signals are contradictory: for example, one metric says a map is biased towards Democrats while another metric say the same map is bia…

Ein Artikel in Plos One beleuchtet den politischen #Bias von #Chatbots und zeigt, dass viele KI-Modelle eher linksgerichtete Antworten geben.
Dies könnte auf das Supervised Fine-Tuning zurückzuführen sein, bei dem KI durch menschliche Beispiele lernt. Interessanterweise weisen unterschiedliche

Linksgerichtete Chatbots? Eine Analyse der politischen Ausrichtung von LLMs
Politischer Bias in ChatGPT und Co.: Gibt es eine politische Ausrichtung in LLMs und wodurch könnte sie entstehen?

An investigation finds rampant caste bias in ChatGPT and Sora; a researcher also finds caste bias in Sarvam AI, which touts itself as a sovereign AI for India (Nilesh Christopher/MIT Technology Review)
https://www.technologyreview.com/2025/10/01/1124621/openai-in…

OpenAI is huge in India. Its models are steeped in caste bias.
India is OpenAI’s second-largest market, but ChatGPT and Sora reproduce caste stereotypes that harm millions of people.

Supreme Court will hear appeal of Black death row inmate over racial bias in Mississippi jury makeup (Mark Sherman/Associated Press)
https://apnews.com/article/supreme-court-mississippi-racial-bias-jury-2ad7c6c707471ec891eeee66decb4c1b
http://www.memeorandum.com/251215/p71#a251215p71

Senator Marsha Blackburn said
“I look forward to introducing the
"TRUMP AMERICA AI Act"
in the new year to create one federal rulebook for AI
to protect children, creators, conservatives, and communities"
Combats the consistent pattern of bias against conservative figures demonstrated by Big Tech and AI systems by requiring:
Audits of high-risk AI systems to undergo regular bias evaluations to prevent discrimination based on protected characte…

Blackburn Unveils National Policy Framework for Artificial Intelligence

The Hidden Bias: A Study on Explicit and Implicit Political Stereotypes in Large Language Models
Konrad L\"ohr, Shuzhou Yuan, Michael F\"arber
https://arxiv.org/abs/2510.08236

The Hidden Bias: A Study on Explicit and Implicit Political Stereotypes in Large Language Models
Large Language Models (LLMs) are increas- ingly integral to information dissemination and decision-making processes. Given their grow- ing societal influence, understanding potential biases, particularly within the political domain, is crucial to prevent undue influence on public opinion and democratic processes. This work investigates political bias and stereotype propa- gation across eight prominent LLMs using the two-dimensional Political Compass Test (PCT). Initially, the PCT is employed to…

Dr. Bias: Social Disparities in AI-Powered Medical Guidance
Emma Kondrup, Anne Imouza
https://arxiv.org/abs/2510.09162 https://arxiv.org/pdf/2510.09162

Dr. Bias: Social Disparities in AI-Powered Medical Guidance
With the rapid progress of Large Language Models (LLMs), the general public now has easy and affordable access to applications capable of answering most health-related questions in a personalized manner. These LLMs are increasingly proving to be competitive, and now even surpass professionals in some medical capabilities. They hold particular promise in low-resource settings, considering they provide the possibility of widely accessible, quasi-free healthcare support. However, evaluations that …

Lawsuit claims city of Dallas can't ban anti-LGBTQ bias or regulate cat breeding - LGBTQ Nation
https://www.lgbtqnation.com/2025/10/lawsuit-claims-city-of-dallas-cant-ban-anti-lgbtq-bias-or-regulate-cat-breeding/

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

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

An LLM generates a statistically typical response.
If you ask an LLM whether the world is round, it generates what would be a typical response to the question of whether the world is round. (It will probably be correct, because the typical answer to that question is the correct one.)
If you ask an LLM to provide examples of its own bias, it will generate what would be a typical response to a request to provide examples of bias.
In neither case is the LLM sharing any knowledge or insight other than what is typical.
2/

Bias-Reduced Estimation of Structural Equation Models
Haziq Jamil, Yves Rosseel, Oliver Kemp, Ioannis Kosmidis
https://arxiv.org/abs/2509.25419 https://arx…

Bias-Reduced Estimation of Structural Equation Models
Finite-sample bias is a pervasive challenge in the estimation of structural equation models (SEMs), especially when sample sizes are small or measurement reliability is low. A range of methods have been proposed to improve finite-sample bias in the SEM literature, ranging from analytic bias corrections to resampling-based techniques, with each carrying trade-offs in scope, computational burden, and statistical performance. We apply the reduced-bias M-estimation framework (RBM, Kosmidis & Lunard…

The Current State of AI Bias Bounties: An Overview of Existing Programmes and Research
Sergej Kucenko, Nathaniel Dennler, Fengxiang He
https://arxiv.org/abs/2510.02036 https://

The Current State of AI Bias Bounties: An Overview of Existing Programmes and Research
Current bias evaluation methods rarely engage with communities impacted by AI systems. Inspired by bug bounties, bias bounties have been proposed as a reward-based method that involves communities in AI bias detection by asking users of AI systems to report biases they encounter when interacting with such systems. In the absence of a state-of-the-art review, this survey aimed to identify and analyse existing AI bias bounty programmes and to present academic literature on bias bounties. Google, …

Bridging the Physics-Data Gap with FNO-Guided Conditional Flow Matching: Designing Inductive Bias through Hierarchical Physical Constraints
Tsuyoshi Okita
https://arxiv.org/abs/2510.08295

Bridging the Physics-Data Gap with FNO-Guided Conditional Flow Matching: Designing Inductive Bias through Hierarchical Physical Constraints
Conventional time-series generation often ignores domain-specific physical constraints, limiting statistical and physical consistency. We propose a hierarchical framework that embeds the inherent hierarchy of physical laws-conservation, dynamics, boundary, and empirical relations-directly into deep generative models, introducing a new paradigm of physics-informed inductive bias. Our method combines Fourier Neural Operators (FNOs) for learning physical operators with Conditional Flow Matching (C…

Not in Sync: Unveiling Temporal Bias in Audio Chat Models
Jiayu Yao, Shenghua Liu, Yiwei Wang, Rundong Cheng, Lingrui Mei, Baolong Bi, Zhen Xiong, Xueqi Cheng
https://arxiv.org/abs/2510.12185

Not in Sync: Unveiling Temporal Bias in Audio Chat Models
Large Audio Language Models (LALMs) are increasingly applied to audio understanding and multimodal reasoning, yet their ability to locate when events occur remains underexplored. We present the first systematic study of temporal bias in LALMs, revealing a key limitation in their timestamp prediction. For example, when asked "At which second does the lecturer introduce the key formula?", models often predict timestamps that are consistently earlier or later than the ground truth. Through control…

Source: the BBC made changes to content and editorial oversight at its BBC Arabic network amid an internal debate about "anti-Israel bias" (Jake Kanter/Deadline)
https://deadline.com/2025/11/bbc-arabic-oversight-reworked-anti-israel-bias-1236…

BBC Overhauled Arabic Network Output & Oversight As Internal Debate Raged About Anti-Israel Bias
The BBC undertook a "complete reworking" of the editorial oversight at BBC Arabic after an report identified "issues" output amid the Gaza conflict.

Microsoft signs a deal with a legal advocacy group to resolve claims it denied discounts to conservative religious nonprofits; OpenAI and Asana also made deals (Jeff Green/Bloomberg)
https://www.bloomberg.com/news/articles/20

House Republicans held a hearing Wednesday broadcasting long-standing conservative allegations of a left-wing bias in the small, prestigious Truman Scholarship program.
Witnesses called by the GOP said the winners disproportionately espouse causes such as promoting racial justice and fighting climate change
—and wind up working for Democrats and left-leaning organizations
—while few recipients profess interest in conservative aims.
But rather than counter the allegations…

House Republicans Accuse Truman Scholarship of Liberal Bias
Republicans held a hearing airing accusations that the prestigious program heavily favors left-leaning candidates. No one from the scholarship foundation was invited.

RFK Jr. is very worried about anti-Christian bias at HHS...under Biden (Marisa Kabas/The Handbasket)
https://www.thehandbasket.co/p/rfk-jr-anti-christian-bias-hhs-survey-biden
http://www.memeorandum.com/251202/p176#a251202p176

Spatially-informed transformers: Injecting geostatistical covariance biases into self-attention for spatio-temporal forecasting
Yuri Calleo
https://arxiv.org/abs/2512.17696 https://arxiv.org/pdf/2512.17696 https://arxiv.org/html/2512.17696
arXiv:2512.17696v1 Announce Type: new
Abstract: The modeling of high-dimensional spatio-temporal processes presents a fundamental dichotomy between the probabilistic rigor of classical geostatistics and the flexible, high-capacity representations of deep learning. While Gaussian processes offer theoretical consistency and exact uncertainty quantification, their prohibitive computational scaling renders them impractical for massive sensor networks. Conversely, modern transformer architectures excel at sequence modeling but inherently lack a geometric inductive bias, treating spatial sensors as permutation-invariant tokens without a native understanding of distance. In this work, we propose a spatially-informed transformer, a hybrid architecture that injects a geostatistical inductive bias directly into the self-attention mechanism via a learnable covariance kernel. By formally decomposing the attention structure into a stationary physical prior and a non-stationary data-driven residual, we impose a soft topological constraint that favors spatially proximal interactions while retaining the capacity to model complex dynamics. We demonstrate the phenomenon of ``Deep Variography'', where the network successfully recovers the true spatial decay parameters of the underlying process end-to-end via backpropagation. Extensive experiments on synthetic Gaussian random fields and real-world traffic benchmarks confirm that our method outperforms state-of-the-art graph neural networks. Furthermore, rigorous statistical validation confirms that the proposed method delivers not only superior predictive accuracy but also well-calibrated probabilistic forecasts, effectively bridging the gap between physics-aware modeling and data-driven learning.
toXiv_bot_toot

From Delegates to Trustees: How Optimizing for Long-Term Interests Shapes Bias and Alignment in LLM
Suyash Fulay, Jocelyn Zhu, Michiel Bakker
https://arxiv.org/abs/2510.12689 ht…

From Delegates to Trustees: How Optimizing for Long-Term Interests Shapes Bias and Alignment in LLM
Large language models (LLMs) have shown promising accuracy in predicting survey responses and policy preferences, which has increased interest in their potential to represent human interests in various domains. Most existing research has focused on behavioral cloning, effectively evaluating how well models reproduce individuals' expressed preferences. Drawing on theories of political representation, we highlight an underexplored design trade-off: whether AI systems should act as delegates, mirr…

Tokenization Disparities as Infrastructure Bias: How Subword Systems Create Inequities in LLM Access and Efficiency
Hailay Kidu Teklehaymanot, Wolfgang Nejdl
https://arxiv.org/abs/2510.12389

Tokenization Disparities as Infrastructure Bias: How Subword Systems Create Inequities in LLM Access and Efficiency
Tokenization disparities pose a significant barrier to achieving equitable access to artificial intelligence across linguistically diverse populations. This study conducts a large-scale cross-linguistic evaluation of tokenization efficiency in over 200 languages to systematically quantify computational inequities in large language models (LLMs). Using a standardized experimental framework, we applied consistent preprocessing and normalization protocols, followed by uniform tokenization through …

OpenAI says GPT‑5 instant and GPT‑5 thinking cut political bias by 30% from earlier models, and show greater robustness to charged prompts (Ashley Gold/Axios)
https://www.axios.com/2025/10/09/openai-gpt-5-least-biased-model

OpenAI says GPT-5 is its least biased model yet
New findings from OpenAI researchers found that "charged" prompts produced the most biased results.

Fisher Information, Training and Bias in Fourier Regression Models
Lorenzo Pastori, Veronika Eyring, Mierk Schwabe
https://arxiv.org/abs/2510.06945 https://

Fisher Information, Training and Bias in Fourier Regression Models
Motivated by the growing interest in quantum machine learning, in particular quantum neural networks (QNNs), we study how recently introduced evaluation metrics based on the Fisher information matrix (FIM) are effective for predicting their training and prediction performance. We exploit the equivalence between a broad class of QNNs and Fourier models, and study the interplay between the \emph{effective dimension} and the \emph{bias} of a model towards a given task, investigating how these affe…

Trump goes to war with 'fake news' BBC (Telegraph)
https://www.telegraph.co.uk/us/news/2025/11/07/donald-trump-attacks-bbc-bias-jan-6-speech/
http://www.memeorandum.com/251108/p1#a251108p1

BBC DG Tim Davie and BBC News head Deborah Turness resign, after BBC was accused of "serious and systemic" bias on topics like Trump, Gaza, and trans rights (Michael Savage/The Guardian)
https://www.theguardian.com/media/2025/nov/09/t…

Trump’s White House is taking on the role of media critic and asking for help from “everyday Americans.”
Over the Thanksgiving weekend, the White House launched a web portal it says will spotlight bias on the part of news outlets,
targeting the Boston Globe, CBS News and the Independent as its inaugural “media offender of the week.”
It’s the latest wrinkle in the fight against the First Amendment
-- what Trump, in his first term, labeled “fake news.”
The Republi…

Bias-Aware AI Chatbot for Engineering Advising at the University of Maryland A. James Clark School of Engineering
Prarthana P. Kartholy, Thandi M. Labor, Neil N. Panchal, Sean H. Wang, Hillary N. Owusu
https://arxiv.org/abs/2510.09636

Bias-Aware AI Chatbot for Engineering Advising at the University of Maryland A. James Clark School of Engineering
Selecting a college major is a difficult decision for many incoming freshmen. Traditional academic advising is often hindered by long wait times, intimidating environments, and limited personalization. AI Chatbots present an opportunity to address these challenges. However, AI systems also have the potential to generate biased responses, prejudices related to race, gender, socioeconomic status, and disability. These biases risk turning away potential students and undermining reliability of AI s…

Mitigating Judgment Preference Bias in Large Language Models through Group-Based Polling
Shuliang Liu, Zhipeng Xu, Zhenghao Liu, Yukun Yan, Minghe Yu, Yu Gu, Chong Chen, Huiyuan Xie, Ge Yu
https://arxiv.org/abs/2510.08145

Mitigating Judgment Preference Bias in Large Language Models through Group-Based Polling
Large Language Models (LLMs) as automatic evaluators, commonly referred to as LLM-as-a-Judge, have also attracted growing attention. This approach plays a vital role in aligning LLMs with human judgments, providing accurate and reliable assessments. However, LLM-based judgment models often exhibit judgment preference bias during the evaluation phase, tending to favor responses generated by themselves, undermining the reliability of their judgments. This paper introduces the Group-Based Polling …

Hundreds of ex-DOJ staffers demand oversight ahead of Pam Bondi hearing (Josephine Walker/Axios)
https://www.axios.com/2025/10/06/doj-trump-pam-bondi-criticized-political-bias-loyalty
http://www.memeorandum.com/251006/p138#a251006p138

Sony unveils the Fair Human-Centric Image Benchmark dataset to test the fairness of computer vision models, saying it was compiled in a fair and ethical manner (Thomas Claburn/The Register)
https://www.theregister.com/2025/11/05/sony_ai_vision_model_benchmark/…

Sony rolls out a standard way to measure bias in how AI describes what it 'sees'
: Images in the test dataset were all sourced with consent

HALF: Harm-Aware LLM Fairness Evaluation Aligned with Deployment
Ali Mekky, Omar El Herraoui, Preslav Nakov, Yuxia Wang
https://arxiv.org/abs/2510.12217 https://

HALF: Harm-Aware LLM Fairness Evaluation Aligned with Deployment
Large language models (LLMs) are increasingly deployed across high-impact domains, from clinical decision support and legal analysis to hiring and education, making fairness and bias evaluation before deployment critical. However, existing evaluations lack grounding in real-world scenarios and do not account for differences in harm severity, e.g., a biased decision in surgery should not be weighed the same as a stylistic bias in text summarization. To address this gap, we introduce HALF (Harm-A…

How ideology-driven AI chatbots like Grok and Gab's Arya position themselves as alternatives to mainstream chatbots accused of liberal bias (New York Times)
https://www.nytimes.com/2025/11/04/busines

Right-Wing Chatbots Turbocharge America’s Political and Cultural Wars
Once pitched as dispassionate tools to answer your questions, A.I. chatbots are now programmed to reflect the biases of their creators.

IndiCASA: A Dataset and Bias Evaluation Framework in LLMs Using Contrastive Embedding Similarity in the Indian Context
Santhosh G S, Akshay Govind S, Gokul S Krishnan, Balaraman Ravindran, Sriraam Natarajan
https://arxiv.org/abs/2510.02742

IndiCASA: A Dataset and Bias Evaluation Framework in LLMs Using Contrastive Embedding Similarity in the Indian Context
Large Language Models (LLMs) have gained significant traction across critical domains owing to their impressive contextual understanding and generative capabilities. However, their increasing deployment in high stakes applications necessitates rigorous evaluation of embedded biases, particularly in culturally diverse contexts like India where existing embedding-based bias assessment methods often fall short in capturing nuanced stereotypes. We propose an evaluation framework based on a encoder …

Probing Social Identity Bias in Chinese LLMs with Gendered Pronouns and Social Groups
Geng Liu, Feng Li, Junjie Mu, Mengxiao Zhu, Francesco Pierri
https://arxiv.org/abs/2510.06974

Probing Social Identity Bias in Chinese LLMs with Gendered Pronouns and Social Groups
Large language models (LLMs) are increasingly deployed in user-facing applications, raising concerns about their potential to reflect and amplify social biases. We investigate social identity framing in Chinese LLMs using Mandarin-specific prompts across ten representative Chinese LLMs, evaluating responses to ingroup ("We") and outgroup ("They") framings, and extending the setting to 240 social groups salient in the Chinese context. To complement controlled experiments, we further analyze Chin…