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Bias

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Biases in human perception of facial age are present and more exaggerated in current AI technology.

Scientific reports
Our estimates of a person's age from their facial appearance suffer from several well-known biases and inaccuracies. Typically, for example, we tend to overestimate the age of smiling faces compared to those with a neutral expression, and the accurac...

Quantification of MR spectra by deep learning in an idealized setting: Investigation of forms of input, network architectures, optimization by ensembles of networks, and training bias.

Magnetic resonance in medicine
PURPOSE: The aims of this work are (1) to explore deep learning (DL) architectures, spectroscopic input types, and learning designs toward optimal quantification in MR spectroscopy of simulated pathological spectra; and (2) to demonstrate accuracy an...

Unconscious Other's Impression Changer: A Method to Manipulate Cognitive Biases That Subtly Change Others' Impressions Positively/Negatively by Making AI Bias in Emotion Estimation AI.

Sensors (Basel, Switzerland)
Artificial Intelligence (AI) for human emotion estimation, such as facial emotion estimation, has been actively studied. On the other hand, there has been little research on unconscious phenomena in cognition and psychology (i.e., cognitive biases) c...

Population codes enable learning from few examples by shaping inductive bias.

eLife
Learning from a limited number of experiences requires suitable inductive biases. To identify how inductive biases are implemented in and shaped by neural codes, we analyze sample-efficient learning of arbitrary stimulus-response maps from arbitrary ...

A Call to Action on Assessing and Mitigating Bias in Artificial Intelligence Applications for Mental Health.

Perspectives on psychological science : a journal of the Association for Psychological Science
Advances in computer science and data-analytic methods are driving a new era in mental health research and application. Artificial intelligence (AI) technologies hold the potential to enhance the assessment, diagnosis, and treatment of people experie...

Application of deep learning models for detection of subdural hematoma: a systematic review and meta-analysis.

Journal of neurointerventional surgery
BACKGROUND: This study aimed to investigate the application of deep learning (DL) models for the detection of subdural hematoma (SDH).

A Flexible Approach for Assessing Heterogeneity of Causal Treatment Effects on Patient Survival Using Large Datasets with Clustered Observations.

International journal of environmental research and public health
Personalized medicine requires an understanding of treatment effect heterogeneity. Evolving toward causal evidence for scenarios not studied in randomized trials necessitates a methodology using real-world evidence. Herein, we demonstrate a methodolo...

Evaluation of machine learning methods for covariate data imputation in pharmacometrics.

CPT: pharmacometrics & systems pharmacology
Missing data create challenges in clinical research because they lead to loss of statistical power and potentially to biased results. Missing covariate data must be handled with suitable approaches to prepare datasets for pharmacometric analyses, suc...