Cognitive bias is widespread, hidden, and difficult to deal with. It impacts each and every aspect of the justice and legal systems, from the initial engagement of police officers attending the crime scene, through the forensic examination, and all t...
Are traditional tests of forecast evaluation well behaved when the competing (nested) model is biased? No, they are not. In this paper, we show analytically and via simulations that, under the null hypothesis of no encompassing, a bias in the nested...
Modern electronic devices, such as smartphones and drones, integrate various sensors to enable diverse sensor-based applications. Yet, sensor measurements exhibit significant variations across different device models, even under the same environment....
Machine learning models are built using training data, which is collected from human experience and is prone to bias. Humans demonstrate a cognitive bias in their thinking and behavior, which is ultimately reflected in the collected data. From Amazon...
Non-random usage of synonymous codons, known as “codon bias”, has been described in many organisms, from bacteria to Drosophila, but little is known about it in phytoplankton. This phenomenon is thought to be driven by selection for trans...
Maximum entropy models have become popular statistical models in neuroscience and other areas in biology and can be useful tools for obtaining estimates of mutual information in biological systems. However, maximum entropy models fit to small data se...
A publication bias has been argued to affect the fate of results in bilingualism research. It was repeatedly suggested that studies presenting evidence for bilingual advantages are more likely to be published compared to studies that do not report re...
Transformer models are now increasingly being used in real-world applications. Indiscriminately using these models as automated tools may propagate biases in ways we do not realize. To responsibly direct actions that will combat this problem, it is o...
Given that some suspected perpetrators were wrongly convicted, a defective bitemark analysis is comparable to dentists’ most crucial clinical decisions regarding assessment. Bias affects human bitemark analysis beyond the limitation of the evid...
This paper describes the use conditions of different polar question constructions in Vietnamese and their consequences for the description and analysis of polar question bias. We argue that the behavior of questions with the final particle à h...
Although deep learning has proven to be tremendously successful, the main issue is the dependency of its performance on the quality and quantity of training datasets. Since the quality of data can be affected by biases, a novel deep learning method b...
Higher-order relationships are a central concept in the science of complex systems. A popular method of attempting to estimate the higher-order relationships of synergy and redundancy from data is through the O-information. It is an information&ndash...
The assessment of risk of bias in systematic reviews and meta-analyses is crucial, as it indicates the accuracy of the synthesized and evaluated data and the validity of the presented results and conclusions. Until now, standardized tools for this pu...
Background: Measles seroprevalence data have potential to be a useful tool for understanding transmission dynamics and for decision making efforts to strengthen immunization programs. In this study, we conducted a systematized review and bias assessm...
Machine learning-based decision support systems bring relief and support to the decision-maker in many domains such as loan application acceptance, dating, hiring, granting parole, insurance coverage, and medical diagnoses. These support systems faci...
The use of algorithmic models or systems in criminal justice is increasing day by day, yet the bias in these algorithms can perpetuate historical inequities, especially in predictive tools like COMPAS. This literature survey examines 30 studies addre...
This study investigates whether cognitive biases such as the endowment effect and status quo bias occur in emissions trading. Such cognitive biases can serve as a barrier to trade. This study’s survey-based experiments, which include hypothetic...
Bias could be defined as the tendency to be in favor or against a person or a group, thus promoting unfairness. In computer science, bias is called algorithmic or artificial intelligence (i.e., AI) and can be described as the tendency to showcase rec...
We analyze the properties of various methods for bias-correcting parameter estimates in both stationary and non-stationary vector autoregressive models. First, we show that two analytical bias formulas from the existing literature are in fact identic...
28 August 2017
Background: Children are believed to be more likely to be reported for maltreatment while they are working with mental health or social service professionals. This “surveillance bias” has been claimed to inflate reporting by fifty percent or more, an...
Exchange bias (EB) is a unidirectional anisotropy occurring in exchange-coupled ferromagnetic/antiferromagnetic systems, such as thin films, core–shell particles, or nanostructures. In addition to a horizontal shift of the hysteresis loop, defi...
Implicit or unconscious bias is commonly proposed to be responsible for women’s underrepresentation in academia. The aim of this scoping review was to identify and discuss the evidence supporting this proposition. Publications about unconscious/impli...
4 April 2018
Research on attentional bias modification has increased since 2014. A recent meta-analysis demonstrates evidence for bias modification for substance disorders, including alcohol and tobacco use disorders. Several pharmacological trials have shown tha...
Surveillance bias arises when differences in the frequency of a condition are due to changes in the modality of detection rather than to a difference in the actual risk of the condition. This bias hampers the surveillance of scrutiny-dependent cancer...
The confirmation bias, unlike other decision biases, has been shown both empirically and in theory to be enhanced with deliberation. This suggests that limited attention, reduced deliberation, or limited available cognitive resources may moderate thi...
Zhang in 2012 introduced a nonparametric estimator of Shannon’s entropy, whose bias decays exponentially fast when the alphabet is finite. We propose a methodology to estimate the bias of this estimator. We then use it to construct a new estimator of...
To reduce the impact of rating bias and popularity bias in recommender system, and make the recommender system reach a balance between recommendation utility and debias effect at the same time, we propose a bi-process debiasing recommendation model b...
Background/Objectives: Internalized weight bias has been linked to adverse mental health outcomes and maladaptive eating-related behaviors. However, its relationship with habitual dietary intake and overall diet quality remains insufficiently explore...
30 September 2016
Eye tracking studies have suggested that, when viewing images centrally presented on a computer screen, observers tend to fixate the middle of the image. This so-called ‘central bias’ was later also observed in mobile eye tracking during outdoors nav...
12 August 2023
Background: Unconscious biases are one of the causes of health disparities. Health professionals have prejudices against patients due to their race, gender, or other factors without their conscious knowledge. This review aimed to provide an overview...
In an inertial navigation system, especially in a pedestrian dead-reckoning system, gyroscope bias can demonstrably reduce positioning accuracy. A novel gyroscope bias estimation algorithm is proposed, which estimates the bias of a gyroscope under an...
The exchange bias (EB) is an effect occurring in coupled ferromagnetic/antiferromagnetic materials of diverse shapes, from core–shell nanoparticles to stacked nanostructures and thin films. The interface coupling typically results in a horizont...
To answer questions, visual question answering systems (VQA) rely on language bias but ignore the information of the images, which has negative information on its generalization. The mainstream debiased methods focus on removing language prior to inf...
3 December 2021
Implicit racial bias is a persistent and pervasive challenge within healthcare education and training settings. A recent systematic review reported that 84% of included studies (31 out of 37) showed evidence of slight to strong pro-white or light ski...
There has been much theoretical work aimed at understanding the evolution of social learning; and in most of it, individual and social learning are treated as distinct processes. A number of authors have argued that this approach is faulty because th...
Haebara and Stocking–Lord linking methods are frequently used to compare the distributions of two groups. Previous research has demonstrated that Haebara and Stocking–Lord linking can produce bias in estimated standard deviations and, to...
Large Language Models (LLMs) inherit societal biases from their training data, potentially leading to harmful outputs. While various techniques aim to mitigate these biases, their effects are typically evaluated only along the targeted dimension, lea...
When one is using climate simulation outputs, one critical issue to consider is the systematic bias affecting the modelled data. The bias correction of modelled data is often used when one is using impact models to assess the effect of climate events...
Future-biased individuals systematically prefer positively valenced events to be in the future (positive future-bias) and negatively valenced events to be in the past (negative future-bias). The most extreme form of future-bias is absolute future-bia...
A common concern with Bayesian methodology in scientific contexts is that inferences can be heavily influenced by subjective biases. As presented here, there are two types of bias for some quantity of interest: bias against and bias in favor. Based u...
Social biases in generative models have gained increasing attention. This paper proposes an automatic evaluation protocol for text-to-image generation, examining how gender bias originates and perpetuates in the generation process of Stable Diffusion...
In this study, we analyze “Discrimination”, ”Bias”, “Fairness”, and “Trustworthiness” as working variables in the context of the social impact of AI. It has been identified that there exists a set of sp...
Objective: U.S. courts have implemented interventions educating jurors about implicit bias, although evidence for their effectiveness remains limited. We explored public perceptions of these interventions that might influence their ability to improve...
The time-varying characteristic of the bias in the GPS code observation is investigated using triple-frequency observations. The method for estimating the combined code bias is presented and the twelve-month (1 January–31 December 2016) triple-...
26 October 2020
In recent years, advances in experimental psychology have led to a better understanding in automatic, unconscious processes, referred to as attentional and approach biases amongst individuals with substance use disorders. Attentional biases refer to...
Research has highlighted racial and socioeconomic disparities for families in child welfare, with calls to address inequities through trainings and structural change. However, few measures have been developed to assess the recognition of racial and c...
The mode bias is present and time-dependent due to imperfect configurations. Data assimilation is the process by which observations are used to correct the model forecast, and is affected by the bias. How to reduce the bias is an important issue. Thi...
Dataset bias is a significant obstacle that negatively affects image classification performance, especially in few-shot learning, where datasets have limited samples per class. However, few studies have focused on this issue. To address this, we prop...
Machine learning models perform face verification (FV) for a variety of highly consequential applications, such as biometric authentication, face identification, and surveillance. Many state-of-the-art FV systems suffer from unequal performance acros...
A recommender system (RS) refers to an agent that recommends items that are suitable for users, and it is implemented through collaborative filtering (CF). CF has a limitation in improving the accuracy of recommendations based on matrix factorization...
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