Tootfinder

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

This https://arxiv.org/abs/2502.13583 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Fundamental Bias in Inverting Random Sampling Matrices with Application to Sub-sampled Newton
A substantial body of work in machine learning (ML) and randomized numerical linear algebra (RandNLA) has exploited various sorts of random sketching methodologies, including random sampling and random projection, with much of the analysis using Johnson--Lindenstrauss and subspace embedding techniques. Recent studies have identified the issue of inversion bias -- the phenomenon that inverses of random sketches are not unbiased, despite the unbiasedness of the sketches themselves. This bias pres…

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

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

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

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

Comment on "Long-range crossed Andreev reflection in a topological insulator nanowire proximitized by a superconductor" by Junya Feng et al
E. S. Tikhonov, V. S. Khrapai
https://arxiv.org/abs/2505.23490

Comment on "Long-range crossed Andreev reflection in a topological insulator nanowire proximitized by a superconductor" by Junya Feng et al
We argue that the interpretation of the experiment [Nature Physics 21, 708-715 (2025)] is misleading in two respects. First, the bias voltages impact the non-local differential conductance randomly, rather than systematically, and the bias symmetry of the non-local conductance in Fig. 3 can be explained by a fine tuned self-gating effect. Second, the full knowledge of the conductance matrix is insufficient to conclude on the relative values of the crossed-Andreev and elastic cotunneling probabi…

This https://arxiv.org/abs/2410.23394 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCY_…

Auditing for Bias in Ad Delivery Using Inferred Demographic Attributes
Auditing social-media algorithms has become a focus of public-interest research and policymaking to ensure their fairness across demographic groups such as race, age, and gender in consequential domains such as the presentation of employment opportunities. However, such demographic attributes are often unavailable to auditors and platforms. When demographics data is unavailable, auditors commonly infer them from other available information. In this work, we study the effects of inference error …

This https://arxiv.org/abs/2412.04629 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

Argumentative Experience: Reducing Confirmation Bias on Controversial Issues through LLM-Generated Multi-Persona Debates
Large language models (LLMs) are enabling designers to give life to exciting new user experiences for information access. In this work, we present a system that generates LLM personas to debate a topic of interest from different perspectives. How might information seekers use and benefit from such a system? Can centering information access around diverse viewpoints help to mitigate thorny challenges like confirmation bias in which information seekers over-trust search results matching existing …

Fine-Tuning LLMs For ‘Good’ Behavior Makes Them More Likely To Say No https://www.404media.co/fine-tuning-llms-cognitive-bias-decision-making-study/

VHU-Net: Variational Hadamard U-Net for Body MRI Bias Field Correction
Xin Zhu
https://arxiv.org/abs/2506.19181 https://arxiv.org/pdf…

VHU-Net: Variational Hadamard U-Net for Body MRI Bias Field Correction
Bias field artifacts in magnetic resonance imaging (MRI) scans introduce spatially smooth intensity inhomogeneities that degrade image quality and hinder downstream analysis. To address this challenge, we propose a novel variational Hadamard U-Net (VHU-Net) for effective body MRI bias field correction. The encoder comprises multiple convolutional Hadamard transform blocks (ConvHTBlocks), each integrating convolutional layers with a Hadamard transform (HT) layer. Specifically, the HT layer perfo…

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

Contextualized Automatic Speech Recognition with Dynamic Vocabulary Prediction and Activation
Zhennan Lin, Kaixun Huang, Wei Ren, Linju Yang, Lei Xie
https://arxiv.org/abs/2505.23077

Contextualized Automatic Speech Recognition with Dynamic Vocabulary Prediction and Activation
Deep biasing improves automatic speech recognition (ASR) performance by incorporating contextual phrases. However, most existing methods enhance subwords in a contextual phrase as independent units, potentially compromising contextual phrase integrity, leading to accuracy reduction. In this paper, we propose an encoder-based phrase-level contextualized ASR method that leverages dynamic vocabulary prediction and activation. We introduce architectural optimizations and integrate a bias loss to ex…

I feel very undecided about the book I picked up: “Think Like a Monk” by Jay Shetty.
There’s a nagging thought in the back of my mind that the book might not be what I’m looking for. The confirmation bias kicked in immediately and within minutes it called out the following: “Three years after I moved to Mumbai, my teacher, Gauranga Das, told me he believed I would be of greater value and service if I left the ashram and shared what I’d learned with the world”. So these are the teachings of someone who studied for 3 years what others are practicing a life time and then was sent out to be a marketer.

This https://arxiv.org/abs/2410.08948 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csMA_…

Emergent social conventions and collective bias in LLM populations
Social conventions are the backbone of social coordination, shaping how individuals form a group. As growing populations of artificial intelligence (AI) agents communicate through natural language, a fundamental question is whether they can bootstrap the foundations of a society. Here, we present experimental results that demonstrate the spontaneous emergence of universally adopted social conventions in decentralized populations of large language model (LLM) agents. We then show how strong coll…

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…

When Networks Mislead: How Partisan Communication Undermines Democratic Decision-Making
Hsuan-Wei Lee, Po-Kang Hsiao
https://arxiv.org/abs/2506.21820 https…

When Networks Mislead: How Partisan Communication Undermines Democratic Decision-Making
Democratic societies increasingly rely on communication networks to aggregate citizen preferences and information, yet these same networks can systematically mislead voters under certain conditions. We introduce an agent-based model that captures two rival forces in partisan networks: honest noise filtering that lifts accuracy and strategic bluffing that embeds bias. Extensive simulations show that communication architecture shapes voting accuracy more than any individual-level trait. When cand…

This https://arxiv.org/abs/2107.02637 has been replaced.
link: https://scholar.google.com/scholar?q=a

Difference-in-Differences with a Continuous Treatment
This paper analyzes difference-in-differences designs with a continuous treatment. We show that treatment effect on the treated-type parameters can be identified under a generalized parallel trends assumption that is similar to the binary treatment setup. However, interpreting differences in these parameters across different values of the treatment can be particularly challenging due to selection bias that is not ruled out by the parallel trends assumption. We discuss alternative, typically str…

A Silicon Microstrip Detector for Power-Limited and Large Sensitive Area Applications
Dexing Miao, Zijun Xu, Zhiyu Xiang, Pingcheng Liu, Giovanni Ambrosi, Mattia Barbanera, Mengke Cai, Xudong Cai, Hsin-Yi Chou, Matteo Duranti, Valerio Formato, Maria Ionica, Yaozu Jiang, Liangchenglong Jin, Vladimir Koutsenko, Qinze Li, Cong Liu, Xingjian Lv, Alberto Oliva, Wenxi Peng, Rui Qiao, Gianluigi Silvestre, Zibing Wu, Xuhao Yuan, Hongyu Zhang, Xiyuan Zhang, Jianchun Wang

A Silicon Microstrip Detector for Power-Limited and Large Sensitive Area Applications
A silicon microstrip detector (SSD) has been developed to have state of the art spatial resolution and a large sensitive area under stringent power constraints. The design incorporates three floating strips with their bias resistors inserted between two aluminum readout strips. Beam test measurements with the single sensor confirmed that this configuration achieves a total detection efficiency of $99.8 \, \%$ and spatial resolution $7.6 \, \mathrm{μm}$ for MIPs. A double-$η$ algorithm was dev…

Two new NERDS papers: Bias in LLM populations, recommending routes
https://nerds.itu.dk/2025/05/16/two-new-nerds-papers-bias-in-llm-populations-recommending-routes/

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

Ah, frequency illusion bias/Baader-Meinhof phenomenon! I have been learning about the nation-state Treaty of Westphalia stuff.
I'm finding it a bit odd that I'm so late to learn about this ... is it less emphasised in Australia? Why is it emphasised in the US?
At my current stage of learning, Australia's self-definition feels heavily 'state', with any discussion of 'nation' often being tied to racism.
More to learn I guess!
#NationState #TreatyOfWestphalia

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…

Replaced article(s) found for cs.CY. https://arxiv.org/list/cs.CY/new
[1/1]:
- Fairness and Bias in Algorithmic Hiring: a Multidisciplinary Survey
Fabris, Baranowska, Dennis, Graus, Hacker, Saldivar, Borgesius, Biega

ScaleCap: Inference-Time Scalable Image Captioning via Dual-Modality Debiasing
Long Xing, Qidong Huang, Xiaoyi Dong, Pan Zhang, Yuhang Zang, Yuhang Cao, Jinsong Li, Shuangrui Ding, Weiming Zhang, Nenghai Yu, Jiaqi Wang, Feng Wu, Dahua Lin
https://arxiv.org/abs/2506.19848

ScaleCap: Inference-Time Scalable Image Captioning via Dual-Modality Debiasing
This paper presents ScaleCap, an inference-time scalable image captioning strategy that generates comprehensive and detailed image captions. The key challenges of high-quality image captioning lie in the inherent biases of LVLMs: multimodal bias resulting in imbalanced descriptive granularity, offering detailed accounts of some elements while merely skimming over others; linguistic bias leading to hallucinated descriptions of non-existent objects. To address these issues, we propose a scalable …

Saturns, but not super-Jupiters, occur more frequently in the presence of inner super-Earths
Etienne Lefevre-Forjan, Gijs D. Mulders
https://arxiv.org/abs/2506.21204

Saturns, but not super-Jupiters, occur more frequently in the presence of inner super-Earths
Studies from recent years have reached different conclusions regarding how frequently super-Earths are accompanied by long period giant planets and vice versa. This relation has been predicted to be mass dependent by planet formation models. We investigate that as the origin of the discrepancy using a radial velocity sample: the California Legacy Survey. We perform detection completeness corrections in order to discard detection bias as a possible explanation to our results. After bias correcti…

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

Bayesian non-parametric lumping and splitting of nodes in Network Meta-Analysis under heterogeneity
Timothy Disher, Chris Cameron, Brian Hutton
https://arxiv.org/abs/2506.22154

Bayesian non-parametric lumping and splitting of nodes in Network Meta-Analysis under heterogeneity
Network meta-analysis (NMA) synthesizes evidence for multiple treatments, but decisions on node formation can have important statistical implications including bias or inflated uncertainty. Existing data-driven methods often lack flexibility or fail to fully account for node uncertainty and adjust for between-trial heterogeneity simultaneously. We introduce a Bayesian non-parametric framework using a Dirichlet process prior with a regularized horseshoe base measure. This data-driven approach al…

#Harvard continues to ignore reports of anti- Palestinian/Arabi/Muslim bias.
“Now, in the face of Trump, Harvard pretends that it is standing up, but you cannot stand up for freedom and silence members of your own community,”
h…

Harvard Affiliates Deliver Hundreds of Complaints Alleging Anti-Arab and Anti-Muslim Bias to President Garber’s Office | News | The Harvard Crimson
More than 30 Harvard affiliates delivered a box containing 452 discrimination complaints to Massachusetts Hall at a Monday rally, alleging that Harvard has fostered “pervasive bias against Muslims, Arabs and Palestinians.”

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

Stable Minima of ReLU Neural Networks Suffer from the Curse of Dimensionality: The Neural Shattering Phenomenon
Tongtong Liang, Dan Qiao, Yu-Xiang Wang, Rahul Parhi
https://arxiv.org/abs/2506.20779

Stable Minima of ReLU Neural Networks Suffer from the Curse of Dimensionality: The Neural Shattering Phenomenon
We study the implicit bias of flatness / low (loss) curvature and its effects on generalization in two-layer overparameterized ReLU networks with multivariate inputs -- a problem well motivated by the minima stability and edge-of-stability phenomena in gradient-descent training. Existing work either requires interpolation or focuses only on univariate inputs. This paper presents new and somewhat surprising theoretical results for multivariate inputs. On two natural settings (1) generalization g…

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.

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

Systematic bias in LISA ringdown analysis due to waveform inaccuracy
Lodovico Capuano, Massimo Vaglio, Rohit S. Chandramouli, Chantal L Pitte, Adrien Kuntz, Enrico Barausse
https://arxiv.org/abs/2506.21181

Systematic bias in LISA ringdown analysis due to waveform inaccuracy
Inaccurate modeling of gravitational-wave signals can introduce systematic biases in the inferred source parameters. As detector sensitivities improve and signals become louder, mitigating such waveform-induced systematics becomes increasingly important. In this work, we assess the systematic biases introduced by an incomplete description of the ringdown signal from massive black hole binaries in the LISA band. Specifically, we investigate the impact of mode truncation in the ringdown template.…

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

An iterative CMB lensing estimator minimizing instrumental noise bias
Louis Legrand, Blake Sherwin, Anthony Challinor, Julien Carron, Gerrit S. Farren
https://arxiv.org/abs/2506.20667

An iterative CMB lensing estimator minimizing instrumental noise bias
Noise maps from CMB experiments are generally statistically anisotropic, due to scanning strategies, atmospheric conditions, or instrumental effects. Any mis-modeling of this complex noise can bias the reconstruction of the lensing potential and the measurement of the lensing power spectrum from the observed CMB maps. We introduce a new CMB lensing estimator based on the maximum a posteriori (MAP) reconstruction that is minimally sensitive to these instrumental noise biases. By modifying the li…

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…

OptGM: An Optimized Gate Merging Method to Mitigate NBTI in Digital Circuits
Maryam Ghane, Amir M. Hajisadeghi, Hamid R. Zarandi
https://arxiv.org/abs/2506.21487

OptGM: An Optimized Gate Merging Method to Mitigate NBTI in Digital Circuits
This paper presents OptGM, an optimized gate merging method designed to mitigate negative bias temperature instability (NBTI) in digital circuits. First, the proposed approach effectively identifies NBTI-critical internal nodes, defined as those with a signal probability exceeding a predefined threshold. Next, based on the proposed optimized algorithm, the sensitizer gate (which drives the critical node) and the sensitive gate (which is fed by it) are merged into a new complex gate. This comple…

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…

This https://arxiv.org/abs/2304.12647 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_eco…

Learned Collusion
Q-learning can be described as an all-purpose automaton that provides estimates (Q-values) of the continuation values associated with each available action and follows the naive policy of almost always choosing the action with highest Q-value. We consider a family of automata based on Q-values, whose policy may systematically favor some actions over others, for example through a bias that favors cooperation. We look for stable equilibrium biases, easily learned under converging logit/best-respo…

This https://arxiv.org/abs/2505.11370 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Understanding Nonlinear Implicit Bias via Region Counts in Input Space
One explanation for the strong generalization ability of neural networks is implicit bias. Yet, the definition and mechanism of implicit bias in non-linear contexts remains little understood. In this work, we propose to characterize implicit bias by the count of connected regions in the input space with the same predicted label. Compared with parameter-dependent metrics (e.g., norm or normalized margin), region count can be better adapted to nonlinear, overparameterized models, because it is de…

In the case of the Iraq war, the administration of George W Bush believed that Saddam Hussein was building weapons of mass destruction, and
– determined to act on that belief
– sought proof of its proposition.
Convinced that he was right,
Bush sought to streamline information that confirmed its bias.
What fell by the wayside were conflicting views.
Because intelligence is ultimately about assessing the likelihood of things which are difficult to know,

This https://arxiv.org/abs/2402.18306 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

Calibration requirement for Epoch of Reionization 21-cm signal observation -- III. Bias and variance in uGMRT ELAIS-N1 field power spectrum
Power spectrum of \HI 21-cm radiation is one of the promising probes to study large scale structure of the universe and understand galaxy formation and evolution. The presence of foregrounds, that are orders of magnitude larger in the same frequency range of the redshifted 21-cm signal has been one of the largest observational challenges. The foreground contamination also hinders the calibration procedures and introduces residual calibration errors in the interferometric data. It has been shown…

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

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

On the $h$-majority dynamics with many opinions
Francesco d'Amore, Niccol\`o D'Archivio, George Giakkoupis, Emanuele Natale
https://arxiv.org/abs/2506.20218

On the $h$-majority dynamics with many opinions
We present the first upper bound on the convergence time to consensus of the well-known $h$-majority dynamics with $k$ opinions, in the synchronous setting, for $h$ and $k$ that are both non-constant values. We suppose that, at the beginning of the process, there is some initial additive bias towards some plurality opinion, that is, there is an opinion that is supported by $x$ nodes while any other opinion is supported by strictly fewer nodes. We prove that, with high probability, if the bi…

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 …

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…

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…

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…

Advancing global sea ice prediction capabilities using a fully-coupled climate model with integrated machine learning
William Gregory, Mitchell Bushuk, Yong-Fei Zhang, Alistair Adcroft, Laure Zanna, Colleen McHugh, Liwei Jia
https://arxiv.org/abs/2505.18328

Advancing global sea ice prediction capabilities using a fully-coupled climate model with integrated machine learning
We showcase a hybrid modeling framework which embeds machine learning (ML) inference into the GFDL SPEAR climate model, for online sea ice bias correction during a set of global fully-coupled 1-year retrospective forecasts. We compare two hybrid versions of SPEAR to understand the importance of exposing ML models to coupled ice-atmosphere-ocean feedbacks before implementation into fully-coupled simulations: Hybrid_CPL (with feedbacks) and Hybrid_IO (without feedbacks). Relative to SPEAR, Hybrid…

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

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

Coining "#malcompetence" to mean "actions & their outcomes which are consistent with incompetence but which have an overall malicious effect, especially when the incompetence manifests differently in different situations to produced de facto discrimination."
Malcompetence can run the gamut from actual intentional malice disguised to appear as incompetence, to subconsciously coordinated malicious incompetence that the perpetrator is not consciously aware of but which a less subconsciously biased person would not engage in, to truly unintentional incompetence which manifests in a pattern because of bias in oversight/attention/competence-building. These different root causes are often not distinguishable, and sometimes blend together.
<does a quick search>
Okay, so it looks like I'm not the only one to have coined this term, but I think digging into the different types is still useful.

A direct dispersive signature of Pauli spin blockade
Simon Svab, Rafael S. Eggli, Taras Patlatiuk, Miguel J. Carballido. Pierre Chevalier Kwon, Dominique A. Tr\"ussel, Ang Li, Erik P. A. M. Bakkers, Andreas V. Kuhlmann, Dominik M. Zumb\"uhl
https://arxiv.org/abs/2506.20509

A direct dispersive signature of Pauli spin blockade
Pauli Spin Blockade (PSB) is a key paradigm in semiconductor nanostructures and gives access to the spin physics. We report the direct observation of PSB with gate-dispersive reflectometry on double quantum dots with source-drain bias. The reservoir charge transitions are strongly modulated, turning on and off when entering and leaving the blockaded region, consistent with a simple model. Seen with holes in Ge and Si, the effects are enhanced with larger bias voltage and suppressed by magnetic …

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/3lrxgvl677…

Hadamard Encoded Row Column Ultrasonic Expansive Scanning (HERCULES) with Bias-Switchable Row-Column Arrays
Darren Olufemi Dahunsi, Randy Palmar, Tyler Henry, Mohammad Rahim Sobhani, Negar Majidi, Joy Wang, Afshin Kashani Ilkhechi, Jeremy Brown, Roger Zemp
https://arxiv.org/abs/2506.11443

Hadamard Encoded Row Column Ultrasonic Expansive Scanning (HERCULES) with Bias-Switchable Row-Column Arrays
Top-Orthogonal-to-Bottom-Electrode (TOBE) arrays, also known as bias-switchable row-column arrays (RCAs), allow for imaging techniques otherwise impossible for non-bias-switachable RCAs. Hadamard Encoded Row Column Ultrasonic Expansive Scanning (HERCULES) is a novel imaging technique that allows for expansive 3D scanning by transmitting plane or cylindrical wavefronts and receiving using Hadamard-Encoded-Read-Out (HERO) to perform beamforming on what is effectively a full 2D synthetic receive a…

Sources: FTC is discussing a consent decree on the Omnicom-Interpublic merger, prohibiting the combined company from boycotting platforms over political content (New York Times)
https://www.nytimes.com/2025/06/12/business/ftc-omnicom-interpublic-mer…

FTC May Impose Political Bias Rule on Omnicom and Interpublic Merger
The Federal Trade Commission is considering restrictions on Omnicom and Interpublic to prevent the combined company from refusing to place ads on platforms for political reasons.

"Mitigating Bias and Advocating for Data Sovereignty: The Role of Metadata and Paradata in Ethical #AI-Driven Information Systems" https://doi.org/10.1080/19386389.2025.2515766

Alleviating User-Sensitive bias with Fair Generative Sequential Recommendation Model
Yang Liu, Feng Wu, Xuefang Zhu
https://arxiv.org/abs/2506.19777 https:…

Alleviating User-Sensitive bias with Fair Generative Sequential Recommendation Model
Recommendation fairness has recently attracted much attention. In the real world, recommendation systems are driven by user behavior, and since users with the same sensitive feature (e.g., gender and age) tend to have the same patterns, recommendation models can easily capture the strong correlation preference of sensitive features and thus cause recommendation unfairness. Diffusion model (DM) as a new generative model paradigm has achieved great success in recommendation systems. DM's ability …

Computer scientist Greg Nelson and I presented "Following AI's Footsteps from School to Society" at the 2025 UMaine Faculty Institute https://mycampus.maine.edu/web/uc-faculty-portal/faculty-institute-2025 including the IMPACT RISK framew…

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 …

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

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

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 …

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

This https://arxiv.org/abs/2412.04265 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_eco…

On Extrapolation of Treatment Effects in Multiple-Cutoff Regression Discontinuity Designs
Regression discontinuity (RD) designs typically identify the treatment effect at a single cutoff point, which may not always be the primary interest. When and how, then, can we learn about treatment effects away from the cutoff? This paper addresses this question within a multiple-cutoff RD framework. We begin by examining the plausibility of the constant bias assumption proposed by Cattaneo, Keele, Titiunik, and Vazquez-Bare (2021) through the lens of rational decision-making behavior. We argu…

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…

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…

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

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

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…

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.…

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

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

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

ErrorEraser: Unlearning Data Bias for Improved Continual Learning
Xuemei Cao, Hanlin Gu, Xin Yang, Bingjun Wei, Haoyang Liang, Xiangkun Wang, Tianrui Li
https://arxiv.org/abs/2506.09347

ErrorEraser: Unlearning Data Bias for Improved Continual Learning
Continual Learning (CL) primarily aims to retain knowledge to prevent catastrophic forgetting and transfer knowledge to facilitate learning new tasks. Unlike traditional methods, we propose a novel perspective: CL not only needs to prevent forgetting, but also requires intentional forgetting.This arises from existing CL methods ignoring biases in real-world data, leading the model to learn spurious correlations that transfer and amplify across tasks. From feature extraction and prediction resul…

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…

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…

The ESPRESSO transmission spectrum of HD$\,$189733$\,$b : Extracting the planetary sodium and lithium signatures amid stellar contamination
D. Mounzer, W. Dethier, C. Lovis, V. Bourrier, A. Psaridi, H. Chakraborty, M. Lendl, R. Allart, J. V. Seidel, M. R. Zapatero Osorio, P. Molaro, M. Steiner, D. Ehrenreich, Y. Alibert, I. Carleo, S. Cristiani, J. I. Gonz\'alez Hern\'andez, C. J. A. P. Martins, E. Palle, J. Rodrigues, N. Santos, A. Sozzetti, A. Su\'arez Mascare\~no

The ESPRESSO transmission spectrum of HD$\,$189733$\,$b : Extracting the planetary sodium and lithium signatures amid stellar contamination
While transmission spectroscopy has allowed us to detect many atomic and molecular species in exoplanet atmospheres, the improvement in resolution and signal-to-noise ratio enabled us to become sensitive to planet-occulted line distortions (POLDs) in the spectrum that are induced by center-to-limb variations and the Rossiter-McLaughlin effect. POLDs can bias the interpretation of the transmission spectrum, and it is difficult to correct for them with stellar models. We analyzed two ESPRESSO tra…

Towards Two-Stage Counterfactual Learning to Rank
Shashank Gupta, Yiming Liao, Maarten de Rijke
https://arxiv.org/abs/2506.20854 https://

Towards Two-Stage Counterfactual Learning to Rank
Counterfactual learning to rank (CLTR) aims to learn a ranking policy from user interactions while correcting for the inherent biases in interaction data, such as position bias. Existing CLTR methods assume a single ranking policy that selects top-K ranking from the entire document candidate set. In real-world applications, the candidate document set is on the order of millions, making a single-stage ranking policy impractical. In order to scale to millions of documents, real-world ranking syst…

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…

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 …

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…

Bias-Switchable Row-Column Array Imaging using Fast Orthogonal Row-Column Electronic Scanning (FORCES) Compared with Conventional Row-Column Array Imaging
Randy Palamar, Mohammad Rahim Sobhani, Darren Dahunsi, Negar Majidi, Afshin Kashani Ilkhechi, Joy Wang, Jeremy Brown, Roger Zemp
https://arxiv.org/abs/2506.10958

Bias-Switchable Row-Column Array Imaging using Fast Orthogonal Row-Column Electronic Scanning (FORCES) Compared with Conventional Row-Column Array Imaging
Row-Column Arrays (RCAs) offer an attractive alternative to fully wired 2D-arrays for 3D-ultrasound, due to their greatly simplified wiring. However, conventional RCAs face challenges related to their long elements. These include an inability to image beyond the shadow of the aperture and an inability to focus in both transmit and receive for desired scan planes. To address these limitations, we recently developed bias-switchable RCAs, also known as Top Orthogonal to Bottom Electrode (TOBE) arr…

This https://arxiv.org/abs/2302.00469 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_eco…

Regression Adjustment, Cross-Fitting, and Randomized Experiments with Many Controls
This paper studies estimation and inference for average treatment effects in randomized experiments with many covariates, under a design-based framework with a deterministic number of treated units. We show that a simple yet powerful cross-fitted regression adjustment achieves bias-correction and leads to sharper asymptotic properties than existing alternatives. Specifically, we derive higher-order stochastic expansions, analyze associated inference procedures, and propose a modified HC3 varian…

“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.

Adultification Bias in LLMs and Text-to-Image Models
Jane Castleman, Aleksandra Korolova
https://arxiv.org/abs/2506.07282 https://arx…

Adultification Bias in LLMs and Text-to-Image Models
The rapid adoption of generative AI models in domains such as education, policing, and social media raises significant concerns about potential bias and safety issues, particularly along protected attributes, such as race and gender, and when interacting with minors. Given the urgency of facilitating safe interactions with AI systems, we study bias along axes of race and gender in young girls. More specifically, we focus on "adultification bias," a phenomenon in which Black girls are presumed t…

Filters of Identity: AR Beauty and the Algorithmic Politics of the Digital Body
Miriam Doh, Corinna Canali, Nuria Oliver
https://arxiv.org/abs/2506.19611 h…

Filters of Identity: AR Beauty and the Algorithmic Politics of the Digital Body
This position paper situates AR beauty filters within the broader debate on Body Politics in HCI. We argue that these filters are not neutral tools but technologies of governance that reinforce racialized, gendered, and ableist beauty standards. Through naming conventions, algorithmic bias, and platform governance, they impose aesthetic norms while concealing their influence. To address these challenges, we advocate for transparency-driven interventions and a critical rethinking of algorithmic …

Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias
Yuanzhe Hu, Kinshuk Goel, Vlad Killiakov, Yaoqing Yang
https://arxiv.org/abs/2506.06280

Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias
Diagnosing deep neural networks (DNNs) through the eigenspectrum of weight matrices has been an active area of research in recent years. At a high level, eigenspectrum analysis of DNNs involves measuring the heavytailness of the empirical spectral densities (ESD) of weight matrices. It provides insight into how well a model is trained and can guide decisions on assigning better layer-wise training hyperparameters. In this paper, we address a challenge associated with such eigenspectrum methods:…

Principal stratification with recurrent events truncated by a terminal event: A nested Bayesian nonparametric approach
Yuki Ohnishi, Michael O. Harhay, Fan Li
https://arxiv.org/abs/2506.19015

Principal stratification with recurrent events truncated by a terminal event: A nested Bayesian nonparametric approach
Recurrent events often serve as key endpoints in clinical studies but may be prematurely truncated by terminal events such as death, creating selection bias and complicating causal inference. To address this challenge, we propose novel causal estimands within the principal stratification framework, introducing a refined ``always-survivor'' stratum that defines survival until the final recurrent event rather than a fixed time point, yielding more stable and interpretable causal contrasts. We dev…

Non-Separable Halo Bias from High-Redshift Galaxy Clustering
Emy Mons, Vipul Prasad Maranchery, M. S. Suryan Sivadas, Charles Jose
https://arxiv.org/abs/2506.07662

Non-Separable Halo Bias from High-Redshift Galaxy Clustering
The halo model provides a powerful framework for interpreting galaxy clustering by linking the spatial distribution of dark matter haloes to the underlying matter distribution. A key assumption within the linear bias approximation of the halo model is that the halo bias between two halo populations is a separable function of the mass of each population. In this work, we test the validity of the separable bias approximation on quasi-linear scales using both simulations and observational data. Un…

DiffPR: Diffusion-Based Phase Reconstruction via Frequency-Decoupled Learning
Yi Zhang
https://arxiv.org/abs/2506.11183 https://arxiv…

DiffPR: Diffusion-Based Phase Reconstruction via Frequency-Decoupled Learning
Oversmoothing remains a persistent problem when applying deep learning to off-axis quantitative phase imaging (QPI). End-to-end U-Nets favour low-frequency content and under-represent fine, diagnostic detail. We trace this issue to spectral bias and show that the bias is reinforced by high-level skip connections that feed high-frequency features directly into the decoder. Removing those deepest skips thus supervising the network only at a low resolution significantly improves generalisation and…

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.

Correcting for Position Bias in Learning to Rank: A Control Function Approach
Md Aminul Islam, Kathryn Vasilaky, Elena Zheleva
https://arxiv.org/abs/2506.06989

Correcting for Position Bias in Learning to Rank: A Control Function Approach
Implicit feedback data, such as user clicks, is commonly used in learning-to-rank (LTR) systems because it is easy to collect and it often reflects user preferences. However, this data is prone to various biases, and training an LTR system directly on biased data can result in suboptimal ranking performance. One of the most prominent and well-studied biases in implicit feedback data is position bias, which occurs because users are more likely to interact with higher-ranked documents regardless …

Bias and Identifiability in the Bounded Confidence Model
Claudio Borile, Jacopo Lenti, Valentina Ghidini, Corrado Monti, Gianmarco De Francisci Morales
https://arxiv.org/abs/2506.11751

Bias and Identifiability in the Bounded Confidence Model
Opinion dynamics models such as the bounded confidence models (BCMs) describe how a population can reach consensus, fragmentation, or polarization, depending on a few parameters. Connecting such models to real-world data could help understanding such phenomena, testing model assumptions. To this end, estimation of model parameters is a key aspect, and maximum likelihood estimation provides a principled way to tackle it. Here, our goal is to outline the properties of statistical estimators of th…

This https://arxiv.org/abs/2506.00253 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

Aligned but Blind: Alignment Increases Implicit Bias by Reducing Awareness of Race
Although value-aligned language models (LMs) appear unbiased in explicit bias evaluations, they often exhibit stereotypes in implicit word association tasks, raising concerns about their fair usage. We investigate the mechanisms behind this discrepancy and find that alignment surprisingly amplifies implicit bias in model outputs. Specifically, we show that aligned LMs, unlike their unaligned counterparts, overlook racial concepts in early internal representations when the context is ambiguous. …

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

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

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…

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

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