Tootfinder

Training to reduce cognitive bias may improve decision making after all https://theconversation.com/training-to-reduce-cognitive-bias-may-improve-decision-making-after-all-126402

Training to reduce cognitive bias may improve decision making after all
It has long been thought one couldn’t bend one’s intuition. Recent research reveals it is in fact possible to reduce bias through training.

This https://arxiv.org/abs/2308.10684 has been replaced.
link: https://scholar.google.com/scholar?q=a

Systematic Offensive Stereotyping (SOS) Bias in Language Models
In this paper, we propose a new metric to measure the SOS bias in language models (LMs). Then, we validate the SOS bias and investigate the effectiveness of removing it. Finally, we investigate the impact of the SOS bias in LMs on their performance and fairness on hate speech detection. Our results suggest that all the inspected LMs are SOS biased. And that the SOS bias is reflective of the online hate experienced by marginalized identities. The results indicate that using debias methods from t…

Here's my latest Human Meme podcast episode: Unveiling Bias: From Shirley Cards to AI - Navigating the Future of Inclusive Technology - http://sites.libsyn.com/84966/unveiling-bias-from-shirley-cards-to-ai-navigatin…

How A.I. Chatbots Become Political (Zvi Mowshowitz/New York Times)
https://www.nytimes.com/interactive/2024/03/28/opinion/ai-political-bias.html
http://www.memeorandum.com/240328/p32#a240328p32

"What determines the political preferences of your #AIchatbot?
Conservatives complain that many commercially available A.I. bots exhibit a persistent liberal bias. Elon Musk built Grok as an alternative language model after grumbling about ChatGPT being a “woke” A.I.
Liberals notice that A.I. output is often—in every sense—insufficiently diverse, because models learn from correlatio…

Opinion | Quiz: How Do Your Politics Stack Up Against ChatGPT’s?
We may soon rely a lot on A.I. chatbots, so keeping an eye on their political leanings is becoming more and more important.

Interesting, some empirical research on how GPT4 /ChatGPT performs in summarizing. Still the low rate of errors can be unacceptable in some contexts. As noted "Life-critical medical decisions should remain based on full, critical, and thoughtful evaluation of the full text of research articles in context with clinical guidelines.".
http…

Quality, Accuracy, and Bias in ChatGPT-Based Summarization of Medical Abstracts
PURPOSE Worldwide clinical knowledge is expanding rapidly, but physicians have sparse time to review scientific literature. Large language models (eg, Chat Generative Pretrained Transformer [ChatGPT]), might help summarize and prioritize research articles to review. However, large language models sometimes “hallucinate” incorrect information. METHODS We evaluated ChatGPT’s ability to summarize 140 peer-reviewed abstracts from 14 journals. Physicians rated the quality, accuracy, and bias …

Sources: Gemini's image generation tool had a technical fix to reduce bias in its outputs but Google didn't anticipate the overcorrection and wasn't transparent (Bloomberg)
https://www.bloomberg.com/news/articles/20

Google Left in ‘Terrible Bind’ By Pulling AI Feature After Right-Wing Backlash
Google tried using a technical fix to reduce bias in a feature that generates realistic-looking images of people. Instead, it set off a new diversity firestorm.

Balanced Similarity with Auxiliary Prompts: Towards Alleviating Text-to-Image Retrieval Bias for CLIP in Zero-shot Learning
Hanyao Wang, Yibing Zhan, Liu Liu, Liang Ding, Jun Yu
https://arxiv.org/abs/2402.18400

Balanced Similarity with Auxiliary Prompts: Towards Alleviating Text-to-Image Retrieval Bias for CLIP in Zero-shot Learning
CLIP has the ability to align texts and images and is nearly the most frequently used foundation model in cross-modal zero-shot learning. However, our experimental findings reveal that CLIP suffers from a bias in text-to-image retrieval, resulting in a decrease in CLIP's zero-shot learning performance. We analytically discover that the bias partly arises from the imbalanced range of similarity scores obtained by CLIP. Accordingly, we propose a Balanced Similarity with Auxiliary Prompts (BSAP) t…

This https://arxiv.org/abs/2305.12829 has been replaced.
link: https://scholar.google.com/scholar?q=a

On Bias and Fairness in NLP: Investigating the Impact of Bias and Debiasing in Language Models on the Fairness of Toxicity Detection
Language models are the new state-of-the-art natural language processing (NLP) models and they are being increasingly used in many NLP tasks. Even though there is evidence that language models are biased, the impact of that bias on the fairness of downstream NLP tasks is still understudied. Furthermore, despite that numerous debiasing methods have been proposed in the literature, the impact of bias removal methods on the fairness of NLP tasks is also understudied. In this work, we investigate t…

A Deep Dive into Effects of Structural Bias on CMA-ES Performance along Affine Trajectories
Niki van Stein, Sarah L. Thomson, Anna V. Kononova
https://arxiv.org/abs/2404.17323

A Deep Dive into Effects of Structural Bias on CMA-ES Performance along Affine Trajectories
To guide the design of better iterative optimisation heuristics, it is imperative to understand how inherent structural biases within algorithm components affect the performance on a wide variety of search landscapes. This study explores the impact of structural bias in the modular Covariance Matrix Adaptation Evolution Strategy (modCMA), focusing on the roles of various modulars within the algorithm. Through an extensive investigation involving 435,456 configurations of modCMA, we identified k…

"The measure would require the regents to set up a department to hear grade challenges based on bias. That department... could determine there was bias and could order the faculty member to regrade the student’s assignment or reevaluate the student’s overall grade." h/t Kevin Reuning @reuning.bsky.social

Students could get grades changed over politics under Arizona Senate plan
Legislation approved by Senate would give university students a new avenue to appeal by alleging "political bias". Teachers could be ordered to change grades.

The SRG/eROSITA All-Sky Survey: Exploring halo assembly bias with X-ray selected superclusters
A. Liu, E. Bulbul, T. Shin, A. von der Linden, V. Ghirardini, M. Kluge, J. S. Sanders, S. Grandis, X. Zhang, E. Artis, Y. E. Bahar, F. Balzer, N. Clerc, N. Malavasi, A. Merloni, K. Nandra, M. E. Ramos-Ceja, S. Zelmer
https://arxiv.org/…

The SRG/eROSITA All-Sky Survey: Exploring halo assembly bias with X-ray selected superclusters
We explore halo assembly bias on cluster scales using large samples of superclusters. Leveraging the largest-ever X-ray galaxy cluster and supercluster samples obtained from the first SRG/eROSITA all-sky survey, we construct two subsamples of galaxy clusters which consist of supercluster members (SC) and isolated clusters (ISO) respectively. After correcting the selection effects on redshift, mass, and survey depth, we compute the excess in the concentration of the intracluster gas of isolated …

Whilst I don't take Intercept stories as gospel this seems well researched though it is repetitive and overly long, on the NY Times Oct 7th piece.
To me this is a case of confirmation bias; the NY Times wanted a sensational story and sent a staffer who doesn't understand confirming sources to manage a stringer that was inexperienced and found nothing but was continually pushed to find it so it became a conspiracy myth.
But it's another violation of the victims.
…

The Story Behind the New York Times October 7 Exposé
“Screams Without Words,” the New York Times story about sexual violence by Jeffrey Gettleman, Adam Sella, and Anat Schwartz comes under fire.

"What determines the political preferences of your #AIchatbot?
Conservatives complain that many commercially available A.I. bots exhibit a persistent liberal bias. Elon Musk built Grok as an alternative language model after grumbling about ChatGPT being a “woke” A.I.
Liberals notice that A.I. output is often—in every sense—insufficiently diverse, because models learn from correlatio…

Opinion | Quiz: How Do Your Politics Stack Up Against ChatGPT’s?
We may soon rely a lot on A.I. chatbots, so keeping an eye on their political leanings is becoming more and more important.

Great article. Great writing, thanks! https://blacktwitter.io/@CuriousScout/112174485630113991

This https://arxiv.org/abs/2312.02959 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Detecting algorithmic bias in medical AI-models
With the growing prevalence of machine learning and artificial intelligence-based medical decision support systems, it is equally important to ensure that these systems provide patient outcomes in a fair and equitable fashion. This paper presents an innovative framework for detecting areas of algorithmic bias in medical-AI decision support systems. Our approach efficiently identifies potential biases in medical-AI models, specifically in the context of sepsis prediction, by employing the Classi…

Calibration requirement for Epoch of Reionization 21-cm signal observation -- III. Bias and variance in uGMRT ELAIS-N1 field power spectrum
Saikat Gayen, Rashmi Sagar, Sarvesh Mangla, Prasun Dutta, Nirupam Roy, Arnab Chakraborty, Jais Kumar, Abhirup Datta, Samir Choudhuri
https://arxiv.org/abs/2402.18306

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. Presence of orders of magnitude larger foregrounds in the frequency range for such observations has been one of the major challenge. The foreground contamination also introduce residual calibration errors in the interferometric data. The latter introduce bias in the 21-cm power spectrum estimates and increase systematics. There have been several efforts to understand and improve …

This https://arxiv.org/abs/2401.08495 has been replaced.
link: https://scholar.google.com/scholar?q=a

Large Language Models Portray Socially Subordinate Groups as More Homogeneous, Consistent with a Bias Observed in Humans
Large language models (LLMs) are becoming pervasive in everyday life, yet their propensity to reproduce biases inherited from training data remains a pressing concern. Prior investigations into bias in LLMs have focused on the association of social groups with stereotypical attributes. However, this is only one form of human bias such systems may reproduce. We investigate a new form of bias in LLMs that resembles a social psychological phenomenon where socially subordinate groups are perceived …

Investigating Bias in LLM-Based Bias Detection: Disparities between LLMs and Human Perception
Luyang Lin, Lingzhi Wang, Jinsong Guo, Kam-Fai Wong
https://arxiv.org/abs/2403.14896 …

Investigating Bias in LLM-Based Bias Detection: Disparities between LLMs and Human Perception
The pervasive spread of misinformation and disinformation in social media underscores the critical importance of detecting media bias. While robust Large Language Models (LLMs) have emerged as foundational tools for bias prediction, concerns about inherent biases within these models persist. In this work, we investigate the presence and nature of bias within LLMs and its consequential impact on media bias detection. Departing from conventional approaches that focus solely on bias detection in m…

Detecting critical treatment effect bias in small subgroups
Piersilvio De Bartolomeis, Javier Abad, Konstantin Donhauser, Fanny Yang
https://arxiv.org/abs/2404.18905

Detecting critical treatment effect bias in small subgroups
Randomized trials are considered the gold standard for making informed decisions in medicine, yet they often lack generalizability to the patient populations in clinical practice. Observational studies, on the other hand, cover a broader patient population but are prone to various biases. Thus, before using an observational study for decision-making, it is crucial to benchmark its treatment effect estimates against those derived from a randomized trial. We propose a novel strategy to benchmark …

FTBC: Forward Temporal Bias Correction for Optimizing ANN-SNN Conversion
Xiaofeng Wu, Velibor Bojkovic, Bin Gu, Kun Suo, Kai Zou
https://arxiv.org/abs/2403.18388

FTBC: Forward Temporal Bias Correction for Optimizing ANN-SNN Conversion
Spiking Neural Networks (SNNs) offer a promising avenue for energy-efficient computing compared with Artificial Neural Networks (ANNs), closely mirroring biological neural processes. However, this potential comes with inherent challenges in directly training SNNs through spatio-temporal backpropagation -- stemming from the temporal dynamics of spiking neurons and their discrete signal processing -- which necessitates alternative ways of training, most notably through ANN-SNN conversion. In this…

When is an Arctic bias not an Arctic bias?
I was going to blog about this cool new paper that my colleagues at DMI have produced, but John Kennedy has as always done such a good job I will just point you over there... Wondering whether a warm bias in the Arctic in ERA5 affects our estimates of global temperature change. When is an Arctic bias not an Arctic bias?

New Study Finds NYTimes Fails to Quote Trans People in 66% of Stories About Anti-Trans Bills, by @… and
Media Matters for America https://www.readtpa.com/p/new-study…

New Study Finds NYT Fails to Quote Trans People in 66% of Stories About Anti-Trans Bills
The paper has been making a bunch of Journalism 101-level mistakes and has a major problem with anti-trans bias.

This https://arxiv.org/abs/2311.14387 has been replaced.
link: https://scholar.google.com/scholar?q=a

Achieving Margin Maximization Exponentially Fast via Progressive Norm Rescaling
In this work, we investigate the margin-maximization bias exhibited by gradient-based algorithms in classifying linearly separable data. We present an in-depth analysis of the specific properties of the velocity field associated with (normalized) gradients, focusing on their role in margin maximization. Inspired by this analysis, we propose a novel algorithm called Progressive Rescaling Gradient Descent (PRGD) and show that PRGD can maximize the margin at an {\em exponential rate}. This stands …

The SRG/eROSITA All-Sky Survey: Exploring halo assembly bias with X-ray selected superclusters
A. Liu, E. Bulbul, T. Shin, A. von der Linden, V. Ghirardini, M. Kluge, J. S. Sanders, S. Grandis, X. Zhang, E. Artis, Y. E. Bahar, F. Balzer, N. Clerc, N. Malavasi, A. Merloni, K. Nandra, M. E. Ramos-Ceja, S. Zelmer
https://arxiv.org/…

The SRG/eROSITA All-Sky Survey: Exploring halo assembly bias with X-ray selected superclusters
We explore halo assembly bias on cluster scales using large samples of superclusters. Leveraging the largest-ever X-ray galaxy cluster and supercluster samples obtained from the first SRG/eROSITA all-sky survey, we construct two subsamples of galaxy clusters which consist of supercluster members (SC) and isolated clusters (ISO) respectively. After correcting the selection effects on redshift, mass, and survey depth, we compute the excess in the concentration of the intracluster gas of isolated …

This https://arxiv.org/abs/2308.14681 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

On the average congruence class bias for cyclicity and divisibility of the groups of $\mathbb{F}_p$-points of elliptic curves
In 2009, W. D. Banks and I. E. Shparlinski studied the average densities of primes $p \leq x$ for which the reductions of elliptic curves of small height modulo $p$ satisfy certain geometric properties, namely cyclicity and divisibility of the number of points by a fixed integer $m$. In this paper, we refine their results, restricting the primes $p$ under consideration to lie in an arithmetic progression $k \bmod{n}$. Furthermore, for a fixed modulus $n$, we investigate statistical biases among…

Wir haben eine kleine Untersuchung zum Addition Bias auf Basis von DeReKoGram im IDS-Sprachreport veröffentlicht.
Dabei haben wir mehrere Gegenstandsbereiche (Größe, Höhe, Wichtigkeit, Wertigkeit ...) in den Blick genommen.
Paper:
https://ids-…

#NYTimesPitchbot
Via Matt McDermott @mattmfm
·
2h
According to the NYT, though Biden is leading his nearest opponent by 76% in the Michigan primary he “faced opposition.” Meanwhile, Trump is leading by 35% but “easily beat” his opponent.
The NYT bias in covering this election is just impossible to ignore, at this point.

Prompting Techniques for Reducing Social Bias in LLMs through System 1 and System 2 Cognitive Processes
Mahammed Kamruzzaman, Gene Louis Kim
https://arxiv.org/abs/2404.17218

Prompting Techniques for Reducing Social Bias in LLMs through System 1 and System 2 Cognitive Processes
Dual process theory posits that human cognition arises via two systems. System 1, which is a quick, emotional, and intuitive process, which is subject to cognitive biases, and System 2, a slow, onerous, and deliberate process. NLP researchers often compare zero-shot prompting in LLMs to System 1 reasoning and chain-of-thought (CoT) prompting to System 2. In line with this interpretation, prior research has found that using CoT prompting in LLMs leads to reduced gender bias. We investigate the r…

Making Them Ask and Answer: Jailbreaking Large Language Models in Few Queries via Disguise and Reconstruction
Tong Liu, Yingjie Zhang, Zhe Zhao, Yinpeng Dong, Guozhu Meng, Kai Chen
https://arxiv.org/abs/2402.18104

Making Them Ask and Answer: Jailbreaking Large Language Models in Few Queries via Disguise and Reconstruction
In recent years, large language models (LLMs) have demonstrated notable success across various tasks, but the trustworthiness of LLMs is still an open problem. One specific threat is the potential to generate toxic or harmful responses. Attackers can craft adversarial prompts that induce harmful responses from LLMs. In this work, we pioneer a theoretical foundation in LLMs security by identifying bias vulnerabilities within the safety fine-tuning and design a black-box jailbreak method named DR…

Österreichs AMS-Chatbot teurer – aber weniger Bias
Der Berufsinfomat des Arbeitsmarktservice in Österreich ist verbessert worden. Dafür kostet der Betrieb auch deutlich mehr als geplant war.
https://www.…

PureLottery: Fair and Bias-Resistant Leader Election with a Novel Single-Elimination Tournament Algorithm
Jonas Ballweg
https://arxiv.org/abs/2402.17459 ht…

PureLottery: Fair and Bias-Resistant Leader Election with a Novel Single-Elimination Tournament Algorithm
Leader Election (LE) is crucial in distributed systems and blockchain technology, ensuring one participant acts as the leader. Traditional LE methods often depend on distributed random number generation (RNG), facing issues like vulnerability to manipulation, lack of fairness, and the need for complex procedures such as verifiable delay functions (VDFs) and publicly-verifiable secret sharing (PVSS). This Bachelor's thesis presents a novel approach to randomized LE, leveraging a game-theoretic a…

Beyond Self-learned Attention: Mitigating Attention Bias in Transformer-based Models Using Attention Guidance
Jiri Gesi, Iftekhar Ahmed
https://arxiv.org/abs/2402.16790

Beyond Self-learned Attention: Mitigating Attention Bias in Transformer-based Models Using Attention Guidance
Transformer-based models have demonstrated considerable potential for source code modeling tasks in software engineering. However, they are limited by their dependence solely on automatic self-attention weight learning mechanisms. Previous studies have shown that these models overemphasize delimiters added by tokenizers (e.g., [CLS], [SEP]), which may lead to overlooking essential information in the original input source code. To address this challenge, we introduce SyntaGuid, a novel approach …

Y’a reckon? 🙄 Walter Sofronoff’s ‘private and secret’ texts with Janet Albrechtsen could lead to impression of bias, court told
Bruce Lehrmann inquiry head’s 273 interactions with the Australian columnist raised in legal challenge by former ACT DPP
h…

This https://arxiv.org/abs/2404.13812 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSI_…

A Comparative Study on Enhancing Prediction in Social Network Advertisement through Data Augmentation
In the ever-evolving landscape of social network advertising, the volume and accuracy of data play a critical role in the performance of predictive models. However, the development of robust predictive algorithms is often hampered by the limited size and potential bias present in real-world datasets. This study presents and explores a generative augmentation framework of social network advertising data. Our framework explores three generative models for data augmentation - Generative Adversaria…

RootInteractive tool for multidimensional statistical analysis, machine learning and analytical model validation
Marian Invanov, Marian Ivanov jr, Giulion Eulise
https://arxiv.org/abs/2403.19330

RootInteractive tool for multidimensional statistical analysis, machine learning and analytical model validation
The ALICE experiment at CERN LHC is specifically designed for investigating heavy ion collisions. The upgraded ALICE accommodates a tenfold increase in PbPb luminosity and a two-order of magnitude surge in minimum bias events. To address the challenges of high detector occupancy and event pile-ups, advanced multidimensional data analysis techniques, including machine learning (ML), are indispensable. Despite ML popularity, the complexity of its models presents interpretation challenges, and ove…

Energy-Aware Heterogeneous Federated Learning via Approximate Systolic DNN Accelerators
Kilian Pfeiffer, Konstantinos Balaskas, Kostas Siozios, J\"org Henkel
https://arxiv.org/abs/2402.18569

Energy-Aware Heterogeneous Federated Learning via Approximate Systolic DNN Accelerators
In Federated Learning (FL), devices that participate in the training usually have heterogeneous resources, i.e., energy availability. In current deployments of FL, devices that do not fulfill certain hardware requirements are often dropped from the collaborative training. However, dropping devices in FL can degrade training accuracy and introduce bias or unfairness. Several works have tacked this problem on an algorithmic level, e.g., by letting constrained devices train a subset of the server …

This https://arxiv.org/abs/2312.11468 has been replaced.
initial toot: https://mastoxiv.page/@arX…

Bias-Reduced Neural Networks for Parameter Estimation in Quantitative MRI
Purpose: To develop neural network (NN)-based quantitative MRI parameter estimators with minimal bias and a variance close to the Cramér-Rao bound. Theory and Methods: We generalize the mean squared error loss to control the bias and variance of the NN's estimates, which involves averaging over multiple noise realizations of the same measurements during training. Bias and variance properties of the resulting NNs are studied for two neuroimaging applications. Results: In simulations, the pr…

I’ll just take this news of ShotSpotter being a creepy police-state surveillance apparatus that operates outside of normal democratic oversight mechanisms as an opportunity to remind everyone that is ShotSpotter is also a mechanism for whitewashing police bias with statistics by taking a map of where police put microphones and presenting it as a supposed map of where gun violence occurs.

This https://arxiv.org/abs/2401.14237 has been replaced.
initial toot: https://mastoxiv.page/@ar…

Short-Time Infrequent Metadynamics for Improved Kinetics Inference
Infrequent Metadynamics is a popular method to obtain the rate of long timescale processes from accelerated simulations. The inference procedure is based on rescaling the first-passage times of Metadynamics trajectories using a bias-dependent acceleration factor. While useful in many cases, it is limited to Poisson kinetics, and a reliable estimation of the unbiased rate requires slow bias deposition and prior knowledge of efficient collective variables. Here, we propose an improved inference s…

"1. Enhanced structured assessments with cognitive testing"
This is basically "racism with a side of logic puzzles"
"Over the past 12 months, we have tested structured assessments for cultural alignment and technical skills, and validated these were predictive of performance at Coinbase"
Measuring performance is itself a can of worms and racism magnet. In order words: garbage in, garbage out. They're validating that they get what they measure, but if both are biased in the same way, well, probably they aren't learning what they think they are.
"We believe having a minimum bar on these assessments will drive consistency in evaluation, reduce bias, and foster a meritocracy."
Consistency? Probably. Consistency of goose turds.
Reduce bias? Certainly the opposite. They're literally creating bias via cultural alignment. That's the whole fucking point of cultural alignment: it's intentional bias.
Oh, did they mean reducing discrimination against protected classes? LOL. also no. Probably they never learned that IQ tests are racist, and what they're doing is probably worse.
"Foster a meritocracy" what is this, a parody of 2005 libertarian tech start-ups? … … oh right, griftocurrency.
Apparently they were sick the day that their grade school teacher presented why meritocracy is a myth
https://en.m.wikipedia.org/wiki/Myth_of_meritocracy

A non-intrusive machine learning framework for debiasing long-time coarse resolution climate simulations and quantifying rare events statistics
Benedikt Barthel Sorensen, Alexis Charalampopoulos, Shixuan Zhang, Bryce Harrop, Ruby Leung, Themistoklis Sapsis
https://arxiv.org/abs/2402.18484

A non-intrusive machine learning framework for debiasing long-time coarse resolution climate simulations and quantifying rare events statistics
Due to the rapidly changing climate, the frequency and severity of extreme weather is expected to increase over the coming decades. As fully-resolved climate simulations remain computationally intractable, policy makers must rely on coarse-models to quantify risk for extremes. However, coarse models suffer from inherent bias due to the ignored "sub-grid" scales. We propose a framework to non-intrusively debias coarse-resolution climate predictions using neural-network (NN) correction operators.…

This https://arxiv.org/abs/2401.05266 has been replaced.
initial toot: https://mastoxiv.page/@arX…

Quantum Modeling of Filter Bubbles Based on Kubo-Matsubara Form Green's Functions Considering Remote and Proximity Interactions:Ultraviolet Divergence to Indefinite Ghosting, Consideration of Cut Surfaces
This research aims to model tracks the evolution of opinions among agents and their collective dynamics, and mathematically represents the resonance of opinions and echo chamber effects within the filter bubble by including non-physical factors such as misinformation and confirmation bias, known as FP ghosting phenomena.The indeterminate ghost phenomenon, a social science concept similar to the uncertainty principle, depicts the variability of social opinion by incorporating information uncerta…

This https://arxiv.org/abs/2312.06695 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Evolving Reservoirs for Meta Reinforcement Learning
Animals often demonstrate a remarkable ability to adapt to their environments during their lifetime. They do so partly due to the evolution of morphological and neural structures. These structures capture features of environments shared between generations to bias and speed up lifetime learning. In this work, we propose a computational model for studying a mechanism that can enable such a process. We adopt a computational framework based on meta reinforcement learning as a model of the interpla…

This https://arxiv.org/abs/2203.12740 has been replaced.
link: https://scholar.google.com/scholar?q=a

Correcting Attrition Bias using Changes-in-Changes
Attrition is a common and potentially important threat to internal validity in treatment effect studies. We extend the changes-in-changes approach to identify the average treatment effect for respondents and the entire study population in the presence of attrition. Our method, which exploits baseline outcome data, can be applied to randomized experiments as well as quasi-experimental difference-in-difference designs. A formal comparison highlights that while widely used corrections typically im…

UNESCO's nvestigation into Gender Bias in Large Language Models: 'LLMs generated negative content about gay subjects in approximately 70% of instances for Llama 2 and in approximately 60% of instances for GPT-2. Finally, in generating content using prompts which intersect gender and culture with occupation, the results highlight a clear bias in AI-generated content, showing a tendency to assign more diverse and professional jobs to men (teacher, doctor, driver), while often re…

Could the Book of Genesis help address gerrymandering?
Partly inspired by a Biblical method of resource allocation, a new paper co-authored by Yale political scientist Kevin DeLuca offers a novel way to reduce partisan bias in legislative districting.
https://…

Doubly robust causal inference through penalized bias-reduced estimation: combining non-probability samples with designed surveys
Jiacong Du, Xu Shi, Donglin Zeng, Bhramar Mukherjee
https://arxiv.org/abs/2403.18039

Doubly robust causal inference through penalized bias-reduced estimation: combining non-probability samples with designed surveys
Causal inference on the average treatment effect (ATE) using non-probability samples, such as electronic health records (EHR), faces challenges from sample selection bias and high-dimensional covariates. This requires considering a selection model alongside treatment and outcome models that are typical ingredients in causal inference. This paper considers integrating large non-probability samples with external probability samples from a design survey, addressing moderately high-dimensional conf…

Europe’s AI Act demands extensive "logs" of users - targets biometrics, bias, black boxes
https://www.thestack.technology/bias-biometrics-and-black-boxes-europes-ai-act-what-you-need-to-know/

A Triplet Track Trigger for the FCC-hh to improve the measurement of Di-Higgs production and the Higgs self-coupling
T. Kar, A. Schöning
https://arXiv.org/abs/2401.16046

A Triplet Track Trigger for the FCC-hh to improve the measurement of Di-Higgs production and the Higgs self-coupling
A new concept, the Triplet Track Trigger (TTT), is proposed for stand-alone tracking at the first trigger level of the FCC-hh detector. The concept is based on a highly scalable monolithic pixel sensor technology and uses a very simple and fast track reconstruction algorithm that can be easily implemented in hardware processors. The goal is to suppress the enormous pileup of ~1000 minimum bias collisions expected at the FCC-hh experiment and to identify the hard-interaction vertex and the corre…

Galaxy clustering at cosmic dawn from JWST/NIRCam observations to redshift z$\sim$11
Nicol\`o Dalmasso, Nicha Leethochawalit, Michele Trenti, Kristan Boyett
https://arxiv.org/abs/2402.18052

Galaxy clustering at cosmic dawn from JWST/NIRCam observations to redshift z$\sim$11
We report measurements of the galaxy two-point correlation function at cosmic dawn, using photometrically-selected sources from the JWST Advanced Deep Extragalactic Survey (JADES). The JWST/NIRCam dataset comprises approximately $N_g \simeq 7000$ photometrically-selected Lyman Break Galaxies (LBGs), spanning from $z=5.5$ up to $z=10.6$. The primary objective of this study is to extend clustering measurements beyond redshift $z>10$, finding a galaxy bias $b=9.6\pm1.7$ for the sample at $\overlin…

Debiasing Text-to-Image Diffusion Models
Ruifei He, Chuhui Xue, Haoru Tan, Wenqing Zhang, Yingchen Yu, Song Bai, Xiaojuan Qi
https://arxiv.org/abs/2402.14577

Debiasing Text-to-Image Diffusion Models
Learning-based Text-to-Image (TTI) models like Stable Diffusion have revolutionized the way visual content is generated in various domains. However, recent research has shown that nonnegligible social bias exists in current state-of-the-art TTI systems, which raises important concerns. In this work, we target resolving the social bias in TTI diffusion models. We begin by formalizing the problem setting and use the text descriptions of bias groups to establish an unsafe direction for guiding the…

Saw this post on the origin on the red bullet hole diagram often used in survivorship bias discussions, and wanted to pass it along: http://www.cameronmoll.com/journal/abraham-wald-red-bullet-holes-origin-story

@… @… besonders darf man bei evolution nie vergessen das dies immer auf die Masse statt dem Individuum betrachtet wird und damit auch immer survivalship bias mitschwingt. Die Menschen die gescheitert sind werden unsichtbar. Da schwingt be…

The Renormalization Group for Large-Scale Structure: Origin of Galaxy Stochasticity
Henrique Rubira, Fabian Schmidt
https://arxiv.org/abs/2404.16929 https:…

The Renormalization Group for Large-Scale Structure: Origin of Galaxy Stochasticity
The renormalization group equations for large-scale structure (RG-LSS) describe how the bias and stochastic (noise) parameters -- both of matter and biased tracers such as galaxies -- evolve as a function of the cutoff $Λ$ of the effective field theory. In previous work, we derived the RG-LSS equations for the bias parameters using the Wilson-Polchinski framework. Here, we extend these results to include stochastic contributions, corresponding to terms in the effective action that are higher o…

This https://arxiv.org/abs/2403.14633 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCY_…

Born With a Silver Spoon? Investigating Socioeconomic Bias in Large Language Models
Socioeconomic bias in society exacerbates disparities, influencing access to opportunities and resources based on individuals' economic and social backgrounds. This pervasive issue perpetuates systemic inequalities, hindering the pursuit of inclusive progress as a society. In this paper, we investigate the presence of socioeconomic bias, if any, in large language models. To this end, we introduce a novel dataset (SilverSpoon), consisting of 3000 samples that illustrate hypothetical scenarios th…

This https://arxiv.org/abs/2403.18346 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

Quantifying and Mitigating Unimodal Biases in Multimodal Large Language Models: A Causal Perspective
Recent advancements in Large Language Models (LLMs) have facilitated the development of Multimodal LLMs (MLLMs). Despite their impressive capabilities, MLLMs often suffer from an over-reliance on unimodal biases (e.g., language bias and vision bias), leading to incorrect answers in complex multimodal tasks. To investigate this issue, we propose a causal framework to interpret the biases in Visual Question Answering (VQA) problems. Within our framework, we devise a causal graph to elucidate the …

Differentiable Pareto-Smoothed Weighting for High-Dimensional Heterogeneous Treatment Effect Estimation
Yoichi Chikahara, Kansei Ushiyama
https://arxiv.org/abs/2404.17483

Differentiable Pareto-Smoothed Weighting for High-Dimensional Heterogeneous Treatment Effect Estimation
There is a growing interest in estimating heterogeneous treatment effects across individuals using their high-dimensional feature attributes. Achieving high performance in such high-dimensional heterogeneous treatment effect estimation is challenging because in this setup, it is usual that some features induce sample selection bias while others do not but are predictive of potential outcomes. To avoid losing such predictive feature information, existing methods learn separate feature representa…

Ofcom warns UK broadcasters on the importance of impartiality ahead of the general election, but stops short of outright banning using politicians as presenters (Daniel Thomas/Financial Times)
https://t.co/ksNyNSpZKP

Human self-destruction is no-doubt part of 'intelligent design', so beloved of the religious. 😉
https://www.newscientist.com/article/mg22329820-200-understand-faulty-thinking-to-tackle-climate-change/

Understand faulty thinking to tackle climate change
The amorphous nature of climate change creates the ideal conditions for human denial and cognitive bias to come to the fore, says George Marshall

Bill Maher: No, no criminal charges, but then he spent 300 pages calling Biden Mr. Magoo, basically. He’s, he’s, this is the words from the report. That Biden’s a well meaning elderly man with diminished faculties and advancing age. Come on. Well, I don’t want to say this thing is loaded, but the final line of the report was, I’m Donald Trump and I approve this message.

Bill Maher Calls Out Robert Hur's Bias In A Single Joke
Bill Maher called the Hur Report loaded and called out the Special Counsel report's bias in his Real Time monologue.

✌🏿 Uber Eats courier’s fight against AI bias shows justice under UK law is hard won
#uber…

Uber Eats courier's fight against AI bias shows justice under UK law is hard won | TechCrunch
On Tuesday, the BBC reported that Uber Eats courier Pa Edrissa Manjang, who is Black, had received a payout from Uber after "racially discriminatory" News this week that an Uber Eats courier accepted a payout to settle legal action after accusing the company of unlawful racial discrimination -- following repeated failures of its facial recognition checks -- raises questions about how fit for purpose UK law is to deal with rising use of AI systems.

How Do Recommendation Models Amplify Popularity Bias? An Analysis from the Spectral Perspective
Siyi Lin, Chongming Gao, Jiawei Chen, Sheng Zhou, Binbin Hu, Can Wang
https://arxiv.org/abs/2404.12008

How Do Recommendation Models Amplify Popularity Bias? An Analysis from the Spectral Perspective
Recommendation Systems (RS) are often plagued by popularity bias. Specifically,when recommendation models are trained on long-tailed datasets, they not only inherit this bias but often exacerbate it. This effect undermines both the precision and fairness of RS and catalyzes the so-called Matthew Effect. Despite the widely recognition of this issue, the fundamental causes remain largely elusive. In our research, we delve deeply into popularity bias amplification. Our comprehensive theoretical an…

Guided By AI: Navigating Trust, Bias, and Data Exploration in AI-Guided Visual Analytics
Sunwoo Ha, Shayan Monadjemi, Alvitta Ottley
https://arxiv.org/abs/2404.14521

Guided By AI: Navigating Trust, Bias, and Data Exploration in AI-Guided Visual Analytics
The increasing integration of artificial intelligence (AI) in visual analytics (VA) tools raises vital questions about the behavior of users, their trust, and the potential of induced biases when provided with guidance during data exploration. We present an experiment where participants engaged in a visual data exploration task while receiving intelligent suggestions supplemented with four different transparency levels. We also modulated the difficulty of the task (easy or hard) to simulate a m…

This https://arxiv.org/abs/2312.13963 has been replaced.
initial toot: https://mastoxiv.page/@…

Biased motility-induced phase separation: from chemotaxis to traffic jams
We propose a one-dimensional model of active particles interpolating between quorum sensing models used in the study of motility-induced phase separation (MIPS) and models of congestion of traffic flow on a single-lane highway. Particles have a target velocity with a density-dependent magnitude and a direction that flips with a finite rate that is biased toward moving right. Two key parameters are the bias and the speed relaxation time. MIPS is known to occur in such models at zero bias and zer…

Points defects produced by irradiation: influence of boundary conditions on their biased elimination
Jean-louis Bocquet
https://arxiv.org/abs/2403.17569 ht…

Points defects produced by irradiation: influence of boundary conditions on their biased elimination
Most of the sinks able to absorb the point defects created under irradiation exhibit a stronger elastic interaction with the interstitial defect I than with the vacancy defect V. This bias of their elastic interaction (EB) is supposed to automatically determine the concomitant absorption bias (AB) leading to an elimination flux of I always larger than the flux of V. We show in this contribution that the link between the EB and the AB is not as rigid as thought previously. On a model system we c…

"Let’s pause here. What would happen if the #NYTimes suddenly hired a #Palestinian filmmaker with no journalistic background, who had recently publicly “liked” posts that called for “pushing Israeli Jews into the sea,” to co-write several of its most sensitive and contested reports?"

Extraordinary charges of bias emerge against NYTimes reporter Anat Schwartz
New doubts are emerging about the New York Times’s coverage of sexual violence in the October 7 attack. The paper must explain why it broke its own rules by hiring a clearly biased writer who endorsed racist and violent rhetoric toward Palestinians.

The Renormalization Group for Large-Scale Structure: Origin of Galaxy Stochasticity
Henrique Rubira, Fabian Schmidt
https://arxiv.org/abs/2404.16929 https:…

The Renormalization Group for Large-Scale Structure: Origin of Galaxy Stochasticity
The renormalization group equations for large-scale structure (RG-LSS) describe how the bias and stochastic (noise) parameters -- both of matter and biased tracers such as galaxies -- evolve as a function of the cutoff $Λ$ of the effective field theory. In previous work, we derived the RG-LSS equations for the bias parameters using the Wilson-Polchinski framework. Here, we extend these results to include stochastic contributions, corresponding to terms in the effective action that are higher o…

This https://arxiv.org/abs/2401.07415 has been replaced.
initial toot: https://mastoxiv.page/@arX…

Alternating Bias Assisted Annealing of Amorphous Oxide Tunnel Junctions
We demonstrate a transformational technique for controllably tuning the electrical properties of fabricated thermally oxidized amorphous aluminum-oxide tunnel junctions. Using conventional test equipment to apply an alternating bias to a heated tunnel barrier, giant increases in the room temperature resistance, greater than 70%, can be achieved. The rate of resistance change is shown to be strongly temperature-dependent, and is independent of junction size in the sub-micron regime. In order to …

> She worries the negative effects will spread as the technology does. "One biased human hiring manager can harm a lot of people in a year, and that's not great," she says. "But an algorithm that is maybe used in all incoming applications at a large company… that could harm hundreds of thousands of applicants."

AI hiring tools may be filtering out the best job applicants
As firms increasingly rely on artificial intelligence-driven hiring platforms, many highly qualified candidates are finding themselves on the cutting room floor.

Star-Image Centering with Deep Learning II: HST/WFPC2 Full Field of View
Dana I. Casetti-Dinescu, Roberto Baena-Galle, Terrence M. Girard, Alejandro Cervantes-Rovira, Sebastian Todeasa
https://arxiv.org/abs/2404.16995

Star-Image Centering with Deep Learning II: HST/WFPC2 Full Field of View
We present an expanded and improved deep-learning (DL) methodology for determining centers of star images on HST/WFPC2 exposures. Previously, we demonstrated that our DL model can eliminate the pixel-phase bias otherwise present in these undersampled images; however that analysis was limited to the central portion of each detector. In the current work we introduce the inclusion of global positions to account for the PSF variation across the entire chip and instrumental magnitudes to account f…

IndiTag: An Online Media Bias Analysis and Annotation System Using Fine-Grained Bias Indicators
Luyang Lin, Lingzhi Wang, Jinsong Guo, Jing Li, Kam-Fai Wong
https://arxiv.org/abs/2403.13446

IndiTag: An Online Media Bias Analysis and Annotation System Using Fine-Grained Bias Indicators
In the age of information overload and polarized discourse, understanding media bias has become imperative for informed decision-making and fostering a balanced public discourse. This paper presents IndiTag, an innovative online media bias analysis and annotation system that leverages fine-grained bias indicators to dissect and annotate bias in digital content. IndiTag offers a novel approach by incorporating large language models, bias indicator, vector database to automatically detect and int…

A General Causal Inference Framework for Cross-Sectional Observational Data
Yonghe Zhao, Huiyan Sun
https://arxiv.org/abs/2404.18197 https://

A General Causal Inference Framework for Cross-Sectional Observational Data
Causal inference methods for observational data are highly regarded due to their wide applicability. While there are already numerous methods available for de-confounding bias, these methods generally assume that covariates consist solely of confounders or make naive assumptions about the covariates. Such assumptions face challenges in both theory and practice, particularly when dealing with high-dimensional covariates. Relaxing these naive assumptions and identifying the confounding covariates…

Evaluation of Geographical Distortions in Language Models: A Crucial Step Towards Equitable Representations
R\'emy Decoupes, Roberto Interdonato, Mathieu Roche, Maguelonne Teisseire, Sarah Valentin
https://arxiv.org/abs/2404.17401

Evaluation of Geographical Distortions in Language Models: A Crucial Step Towards Equitable Representations
Language models now constitute essential tools for improving efficiency for many professional tasks such as writing, coding, or learning. For this reason, it is imperative to identify inherent biases. In the field of Natural Language Processing, five sources of bias are well-identified: data, annotation, representation, models, and research design. This study focuses on biases related to geographical knowledge. We explore the connection between geography and language models by highlighting thei…

Uri Berliner's essay about NPR reignited criticism of the outlet on social media, especially among conservatives, and vehement pushback from some staffers (New York Times)
https://www.nytimes.com/2024/04/11/business/media/npr-criticism-liberal…

NPR in Turmoil After It Is Accused of Liberal Bias
An essay from an editor at the broadcaster has generated a firestorm of criticism about the network on social media, especially among conservatives.

Judge Cannon just showed her 'inexperience' or 'bias' in latest Trump hearing: expert - Raw Story
https://www.rawstory.com/judge-cannon-bias-inexperience/

Judge Cannon just showed her 'inexperience' or 'bias' in latest Trump hearing: expert
Judge Aileen Cannon, the jurist overseeing Donald Trump's criminal case over allegedly stashed confidential documents, showed that she's either in the bag for the former president or that she's too much of a novice on the bench to handle the momentous case, a former prosecutor said on Friday. Andrew...

Building ethical guidelines for generative AI in scientific research
Zhicheng Lin
https://arXiv.org/abs/2401.15284 https://arXiv.org/…

Building ethical guidelines for generative AI in scientific research
Generative artificial intelligence tools like large language models are rapidly transforming academic research and real world applications. However, discussions on ethical guidelines for generative AI in science remain fragmented, underscoring the urgent need for consensus based standards. This paper offers an initial framework by developing analyses and mitigation strategies across five key themes: understanding model limitations regarding truthfulness and bias; respecting privacy, confidentia…

Substrate Scope Contrastive Learning: Repurposing Human Bias to Learn Atomic Representations
Wenhao Gao, Priyanka Raghavan, Ron Shprints, Connor W. Coley
https://arxiv.org/abs/2402.16882

Substrate Scope Contrastive Learning: Repurposing Human Bias to Learn Atomic Representations
Learning molecular representation is a critical step in molecular machine learning that significantly influences modeling success, particularly in data-scarce situations. The concept of broadly pre-training neural networks has advanced fields such as computer vision, natural language processing, and protein engineering. However, similar approaches for small organic molecules have not achieved comparable success. In this work, we introduce a novel pre-training strategy, substrate scope contrasti…

This https://arxiv.org/abs/2401.03855 has been replaced.
link: https://scholar.google.com/scholar?q=a

PythonSaga: Redefining the Benchmark to Evaluate Code Generating LLM
Driven by the surge in code generation using large language models (LLMs), numerous benchmarks have emerged to evaluate these LLMs capabilities. We conducted a large-scale human evaluation of HumanEval and MBPP, two popular benchmarks for Python code generation, analyzing their diversity and difficulty. Our findings unveil a critical bias towards a limited set of programming concepts, neglecting most of the other concepts entirely. Furthermore, we uncover a worrying prevalence of easy tasks, po…

This https://arxiv.org/abs/2402.13391 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

De-Biasing the Bias: Methods for Improving Disparity Assessments with Noisy Group Measurements
Health care decisions are increasingly informed by clinical decision support algorithms, but these algorithms may perpetuate or increase racial and ethnic disparities in access to and quality of health care. Further complicating the problem, clinical data often have missing or poor quality racial and ethnic information, which can lead to misleading assessments of algorithmic bias. We present novel statistical methods that allow for the use of probabilities of racial/ethnic group membership in a…

Clustering Change Sign Detection by Fusing Mixture Complexity
Kento Urano, Ryo Yuki, Kenji Yamanishi
https://arxiv.org/abs/2403.18269 https://

Clustering Change Sign Detection by Fusing Mixture Complexity
This paper proposes an early detection method for cluster structural changes. Cluster structure refers to discrete structural characteristics, such as the number of clusters, when data are represented using finite mixture models, such as Gaussian mixture models. We focused on scenarios in which the cluster structure gradually changed over time. For finite mixture models, the concept of mixture complexity (MC) measures the continuous cluster size by considering the cluster proportion bias and ov…

Uri Berliner's essay about NPR reignited criticism of the outlet on social media, especially among conservatives, and vehement pushback from some staffers (New York Times)
https://www.nytimes.com/2024/04/11/business/media/npr-criticism-liberal…

NPR in Turmoil After It Is Accused of Liberal Bias
An essay from an editor at the broadcaster has generated a firestorm of criticism about the network on social media, especially among conservatives.

This is not news to some folks.
Bias hiding in plain sight: Decades of analyses suggest US media skews anti-#Palestinian | Salon.com
https://www.

Bias hiding in plain sight: Decades of analyses suggest US media skews anti-Palestinian
Anti-Palestinian bias in the U.S. and other countries’ media is often subtle, albeit in plain view.

A proposal to improve the accuracy of cosmological observables and address the Hubble tension problem
Horst Foidl, Tanja Rindler-Daller
https://arXiv.org/abs/2401.15080

A proposal to improve the accuracy of cosmological observables and address the Hubble tension problem
Cosmological observational programs often compare their data not only with $Λ$CDM, but also with dark energy (DE) models, whose time-dependent equations of state (EoS) differ from a cosmological constant. We identified a generic issue in the standard procedure of computing the expansion history for models with time-dependent EoS, which leads to bias in the interpretation of the results. In order to compute the evolution of models with time-dependent EoS parameter $w$ in a consistent manner, we…

Assessing COVID-19 Vaccine Effectiveness in Observational Studies via Nested Trial Emulation
Justin B. DeMonte, Bonnie E. Shook-Sa, Michael G. Hudgens
https://arxiv.org/abs/2403.18115

Assessing COVID-19 Vaccine Effectiveness in Observational Studies via Nested Trial Emulation
Observational data are often used to estimate real-world effectiveness and durability of coronavirus disease 2019 (COVID-19) vaccines. A sequence of nested trials can be emulated to draw inference from such data while minimizing selection bias, immortal time bias, and confounding. Typically, when nested trial emulation (NTE) is employed, effect estimates are pooled across trials to increase statistical efficiency. However, such pooled estimates may lack a clear interpretation when the treatment…

Protected group bias and stereotypes in Large Language Models
Hadas Kotek, David Q. Sun, Zidi Xiu, Margit Bowler, Christopher Klein
https://arxiv.org/abs/2403.14727

Protected group bias and stereotypes in Large Language Models
As modern Large Language Models (LLMs) shatter many state-of-the-art benchmarks in a variety of domains, this paper investigates their behavior in the domains of ethics and fairness, focusing on protected group bias. We conduct a two-part study: first, we solicit sentence continuations describing the occupations of individuals from different protected groups, including gender, sexuality, religion, and race. Second, we have the model generate stories about individuals who hold different types of…

Quantile Outcome Adaptive Lasso: Covariate Selection for Inverse Probability Weighting Estimator of Quantile Treatment Effects
Takehiro Shoji, Jun Tsuchida, Hiroshi Yadohisa
https://arxiv.org/abs/2402.18185

Quantile Outcome Adaptive Lasso: Covariate Selection for Inverse Probability Weighting Estimator of Quantile Treatment Effects
When using the propensity score method to estimate the treatment effects, it is important to select the covariates to be included in the propensity score model. The inclusion of covariates unrelated to the outcome in the propensity score model led to bias and large variance in the estimator of treatment effects. Many data-driven covariate selection methods have been proposed for selecting covariates related to outcomes. However, most of them assume an average treatment effect estimation and may…

Understanding Optimal Feature Transfer via a Fine-Grained Bias-Variance Analysis
Yufan Li, Subhabrata Sen, Ben Adlam
https://arxiv.org/abs/2404.12481 https…

Understanding Optimal Feature Transfer via a Fine-Grained Bias-Variance Analysis
In the transfer learning paradigm models learn useful representations (or features) during a data-rich pretraining stage, and then use the pretrained representation to improve model performance on data-scarce downstream tasks. In this work, we explore transfer learning with the goal of optimizing downstream performance. We introduce a simple linear model that takes as input an arbitrary pretrained feature transform. We derive exact asymptotics of the downstream risk and its fine-grained bias-va…

This https://arxiv.org/abs/2403.07910 has been replaced.
link: https://scholar.google.com/scholar?q=a

MAGPIE: Multi-Task Media-Bias Analysis Generalization for Pre-Trained Identification of Expressions
Media bias detection poses a complex, multifaceted problem traditionally tackled using single-task models and small in-domain datasets, consequently lacking generalizability. To address this, we introduce MAGPIE, the first large-scale multi-task pre-training approach explicitly tailored for media bias detection. To enable pre-training at scale, we present Large Bias Mixture (LBM), a compilation of 59 bias-related tasks. MAGPIE outperforms previous approaches in media bias detection on the Bias …

Investigating Markers and Drivers of Gender Bias in Machine Translations
Peter J Barclay (Edinburgh Napier University), Ashkan Sami (Edinburgh Napier University)
https://arxiv.org/abs/2403.11896

Investigating Markers and Drivers of Gender Bias in Machine Translations
Implicit gender bias in Large Language Models (LLMs) is a well-documented problem, and implications of gender introduced into automatic translations can perpetuate real-world biases. However, some LLMs use heuristics or post-processing to mask such bias, making investigation difficult. Here, we examine bias in LLMss via back-translation, using the DeepL translation API to investigate the bias evinced when repeatedly translating a set of 56 Software Engineering tasks used in a previous study. Ea…

Understanding Optimal Feature Transfer via a Fine-Grained Bias-Variance Analysis
Yufan Li, Subhabrata Sen, Ben Adlam
https://arxiv.org/abs/2404.12481 https…

Understanding Optimal Feature Transfer via a Fine-Grained Bias-Variance Analysis
In the transfer learning paradigm models learn useful representations (or features) during a data-rich pretraining stage, and then use the pretrained representation to improve model performance on data-scarce downstream tasks. In this work, we explore transfer learning with the goal of optimizing downstream performance. We introduce a simple linear model that takes as input an arbitrary pretrained feature transform. We derive exact asymptotics of the downstream risk and its fine-grained bias-va…

Estimating lengths-of-stay of hospitalised COVID-19 patients using a non-parametric model: a case study in Galicia (Spain)
Ana López-Cheda, M. Amalia Jšcome, Ricardo Cao, Pablo M. De Salazar
https://arXiv.org/abs/2401.15259

Estimating lengths-of-stay of hospitalised COVID-19 patients using a non-parametric model: a case study in Galicia (Spain)
Estimating the lengths-of-stay (LoS) of hospitalised COVID-19 patients is key for predicting the hospital beds' demand and planning mitigation strategies, as overwhelming the healthcare systems has critical consequences for disease mortality. However, accurately mapping the time-to-event of hospital outcomes, such as the LoS in the intensive care unit (ICU), requires understanding patient trajectories while adjusting for covariates and observation bias, such as incomplete data. Standard methods…

Locating and Mitigating Gender Bias in Large Language Models
Yuchen Cai, Ding Cao, Rongxi Guo, Yaqin Wen, Guiquan Liu, Enhong Chen
https://arxiv.org/abs/2403.14409

Locating and Mitigating Gender Bias in Large Language Models
Large language models(LLM) are pre-trained on extensive corpora to learn facts and human cognition which contain human preferences. However, this process can inadvertently lead to these models acquiring biases and stereotypes prevalent in society. Prior research has typically tackled the issue of bias through a one-dimensional perspective, concentrating either on locating or mitigating it. This limited perspective has created obstacles in facilitating research on bias to synergistically complem…

This https://arxiv.org/abs/2402.11621 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

Decoding News Narratives: A Critical Analysis of Large Language Models in Framing Bias Detection
This work contributes to the expanding research on the applicability of LLMs in social sciences by examining the performance of GPT-3.5 Turbo, GPT-4, and Flan-T5 models in detecting framing bias in news headlines through zero-shot, few-shot, and explainable prompting methods. A key insight from our evaluation is the notable efficacy of explainable prompting in enhancing the reliability of these models, highlighting the importance of explainable settings for social science research on framing bi…

CosmoMIA: Cosmic Web-based redshift space halo distribution
Daniel Forero-S\'anchez, Francisco-Shu Kitaura, Francesco Sinigaglia, Jose Mar\'ia Coloma-Nodal, Jean-Paul Kneib
https://arxiv.org/abs/2402.17581

CosmoMIA: Cosmic Web-based redshift space halo distribution
Modern galaxy surveys demand extensive survey volumes and resolutions surpassing current dark matter-only simulations' capabilities. To address this, many methods employ effective bias models on the dark matter field to approximate object counts on a grid. However, realistic catalogs necessitate specific coordinates and velocities for a comprehensive understanding of the Universe. In this research, we explore sub-grid modeling to create accurate catalogs, beginning with coarse grid number count…

This https://arxiv.org/abs/2402.16458 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

D-XCB: Data-independent Debiasing for Fair and Accurate Transformer-based Cyberbullying Detection
Swear words are a common proxy to collect datasets with cyberbullying incidents. Our focus is on measuring and mitigating biases derived from spurious associations between swear words and incidents occurring as a result of such data collection strategies. After demonstrating and quantifying these biases, we introduce ID-XCB, the first data-independent debiasing technique that combines adversarial training, bias constraints and debias fine-tuning approach aimed at alleviating model attention to …

Adjusting for Ascertainment Bias in Meta-Analysis of Penetrance for Cancer Risk
Thanthirige Lakshika M. Ruberu, Danielle Braun, Giovanni Parmigiani, Swati Biswas
https://arxiv.org/abs/2402.15030

Adjusting for Ascertainment Bias in Meta-Analysis of Penetrance for Cancer Risk
Multi-gene panel testing allows efficient detection of pathogenic variants in cancer susceptibility genes including moderate-risk genes such as ATM and PALB2. A growing number of studies examine the risk of breast cancer (BC) conferred by pathogenic variants of such genes. A meta-analysis combining the reported risk estimates can provide an overall age-specific risk of developing BC, i.e., penetrance for a gene. However, estimates reported by case-control studies often suffer from ascertainment…

This https://arxiv.org/abs/2309.10978 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Negative Spillover: A Potential Source of Bias in Pragmatic Clinical Trials
Pragmatic clinical trials evaluate the effectiveness of health interventions in real-world settings. Negative spillover can arise in a pragmatic trial if the study intervention affects how scarce resources are allocated between patients in the intervention and comparison groups. This can harm patients assigned to the control group and lead to overestimation of treatment effect. While this type of negative spillover is often addressed in trials of social welfare and public health interventions, …

This https://arxiv.org/abs/2401.01989 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

Revisiting Zero-Shot Abstractive Summarization in the Era of Large Language Models from the Perspective of Position Bias
We characterize and study zero-shot abstractive summarization in Large Language Models (LLMs) by measuring position bias, which we propose as a general formulation of the more restrictive lead bias phenomenon studied previously in the literature. Position bias captures the tendency of a model unfairly prioritizing information from certain parts of the input text over others, leading to undesirable behavior. Through numerous experiments on four diverse real-world datasets, we study position bias…

This https://arxiv.org/abs/2206.11117 has been replaced.
link: https://scholar.google.com/scholar?q=a

Causal inference in multi-cohort studies using the target trial framework to identify and minimize sources of bias
Longitudinal cohort studies, which follow a group of individuals over time, provide the opportunity to examine causal effects of complex exposures on long-term health outcomes. Utilizing data from multiple cohorts has the potential to add further benefit by improving precision of estimates through data pooling and by allowing examination of effect heterogeneity through replication of analyses across cohorts. However, the interpretation of findings can be complicated by biases that may be compou…