Results 81 to 90 of about 16,825 (201)

Catch Me If You Can: The Dynamic Nature of Bias in Machine Learning Applications

Information Systems Journal, EarlyView.
ABSTRACT Bias in machine learning (ML) applications represents systematic differences between expected and actual values of the predicted outputs, such that certain individuals or groups are systematically and disproportionately (dis)advantaged. This paper investigates the dynamic nature of bias in ML applications.
Monideepa Tarafdar, Irina Rets, Yang Hu
wiley   +1 more source

A hard to read font reduces the causality bias

Judgment and Decision Making, 2019
Previous studies have demonstrated that fluency affects judgment and decision-making. The purpose of the present research was to investigate the effect of perceptual fluency in a causal learning task that usually induces an illusion of causality in non ...
Marcos Díaz-Lago, Helena Matute
doaj   +1 more source

Towards Debiasing Code Review Support [PDF]

2025 IEEE/ACM 18th International Conference on Cooperative and Human Aspects of Software Engineering (CHASE)
Cognitive biases appear during code review. They significantly impact the creation of feedback and how it is interpreted by developers. These biases can lead to illogical reasoning and decision-making, violating one of the main hypotheses supporting code review: developers' accurate and objective code evaluation.
Jetzen, Tobias, Devroey, Xavier, Matton, Nicolas, Vanderose, Benoît   +3 more
openaire   +2 more sources

Intrinsic Influences on Medical Emergency Team Call Stand‐Down Decision‐Making: An Observational Study

Journal of Advanced Nursing, EarlyView.
ABSTRACT Aim The aim of this research was to describe factors that influence Intensive Care Unit liaison nurses' decision to stand down a medical emergency team call response. The decision to end a medical emergency team response for a deteriorating patient is referred to as the medical emergency team call stand‐down decision.
Natalie A. Kondos, Jo McDonall, Jonathan Barrett, Tracey Bucknall   +3 more
wiley   +1 more source

A large-scale study and six-month follow-up of an intervention to reduce causal illusions in high school students

Royal Society Open Science
Causal illusions consist of believing that there is a causal relationship between events that are actually unrelated. This bias is associated with pseudoscience, stereotypes and other unjustified beliefs.
Naroa Martínez, Helena Matute, Fernando Blanco, Itxaso Barberia   +3 more
doaj   +1 more source

Modeling and debiasing resource saving judgments

Judgment and Decision Making, 2014
Svenson (2011) showed that choices of one of two alternative productivity increases to save production resources (e.g., man-months) were biased. Judgments of resource savings following a speed increase from a low production speed line were underestimated
Ola Svenson, Nichel Gonzalez, Gabriella Eriksson   +2 more
doaj   +1 more source

Quantifying and Reducing Stereotypes in Word Embeddings [PDF]

, 2016
Machine learning algorithms are optimized to model statistical properties of the training data. If the input data reflects stereotypes and biases of the broader society, then the output of the learning algorithm also captures these stereotypes.
Bolukbasi, Tolga, Chang, Kai-Wei, Kalai, Adam, Saligrama, Venkatesh, Zou, James   +4 more
core  

Econometrics at the Extreme: From Quantile Regression to QFAVAR1

Journal of Economic Surveys, EarlyView.
ABSTRACT This paper surveys quantile modelling from its theoretical origins to current advances. We organize the literature and present core econometric formulations and estimation methods for: (i) cross‐sectional quantile regression; (ii) quantile time series models and their time series properties; (iii) quantile vector autoregressions for ...
Stéphane Goutte, Konstantinos N. Konstantakis, Dimitris Konstantios, Panayotis G. Michaelides, Arsenios‐Georgios N. Prelorentzos   +4 more
wiley   +1 more source

Fair Classification Without Sensitive Attribute Labels via Dynamic Reweighting

Applied Sciences
Fairness-aware classification with respect to sensitive attributes, such as gender and race, is one of the most important topics in machine learning. Although numerous studies have made outstanding progress through various approaches, one key limitation ...
Pilhyeon Lee, Sungho Park
doaj   +1 more source

Predicting suggestive questioning from cognitions and emotions about child sexual abuse across three study paradigms

Legal and Criminological Psychology, EarlyView.
Abstract Purpose Although interviews are individual in nature, and suggestiveness is a major pitfall when questioning children, individual differences in interviewer bias and suggestiveness remain understudied. We assessed relationships between Cognitions and Emotions about Child Sexual Abuse (CECSA) with suggestive questioning and bias across three ...
Elsa Gewehr, Marie Merschhemke, Simone Pülschen, Dietrich Pülschen, Renate Volbert   +4 more
wiley   +1 more source