Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Bias01:22

Bias

5.7K
Bias refers to any tendency that prevents a question from being considered unprejudiced. In research, bias occurs when one outcome or answer is selected or encouraged over others in sampling or testing. Bias can occur during any research phase, including study design, data collection, analysis, and publication.
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...
5.7K
Motivational Bias01:25

Motivational Bias

22
Cognitive bias results from limitations in thinking and information processing, leading to systematic errors in judgment. Conversely, motivational bias stems from personal desires or emotions, causing distortions in perception to align with self-interest. Motivational bias influences how individuals perceive and attribute causes to events, often shaped by personal needs, goals, and self-esteem preservation. This bias can distort judgment, leading to inaccurate assessments of success, failure,...
22
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

746
Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:  
746
Self-Serving Bias01:29

Self-Serving Bias

9
Self-serving bias is a cognitive phenomenon in which individuals attribute positive outcomes to internal factors such as their abilities, intelligence, or effort while attributing negative outcomes to external circumstances. This cognitive distortion helps maintain self-esteem but can also impede objective self-assessment.Theoretical Explanations of Self-Serving BiasTwo primary theories explain the self-serving bias: the cognitive explanation and the motivational explanation.The cognitive...
9
Survival Tree01:19

Survival Tree

172
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
172
Stereotypes, Prejudice, and Discrimination02:55

Stereotypes, Prejudice, and Discrimination

92.0K
Humans are very diverse and although we share many similarities, we also have many differences. The social groups we belong to help form our identities (Tajfel, 1974). These differences may be difficult for some people to reconcile, which may lead to prejudice toward people who are different. Prejudice is a negative attitude and feeling toward an individual based solely on one’s membership in a particular social group (Allport, 1954; Brown, 2010). Prejudice is common against people who...
92.0K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

MRI acquisition and reconstruction cookbook: recipes for reproducibility, served with real-world flavour.

Magma (New York, N.Y.)·2025
Same author

Combining Biology-based and MRI Data-driven Modeling to Predict Response to Neoadjuvant Chemotherapy in Patients with Triple-Negative Breast Cancer.

Radiology. Artificial intelligence·2024
Same author

Code review facility in Magnetic Resonance in Medicine.

Magnetic resonance in medicine·2024
Same author

Deep learning for accelerated and robust MRI reconstruction.

Magma (New York, N.Y.)·2024
Same author

Accelerated motion correction with deep generative diffusion models.

Magnetic resonance in medicine·2024
Same author

Sharing Data Is Essential for the Future of AI in Medical Imaging.

Radiology. Artificial intelligence·2024

Related Experiment Video

Updated: Sep 29, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.6K

Implicit data crimes: Machine learning bias arising from misuse of public data.

Efrat Shimron1, Jonathan I Tamir2,3,4, Ke Wang1

  • 1Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA 94720.

Proceedings of the National Academy of Sciences of the United States of America
|March 21, 2022
PubMed
Summary

Public databases can cause biased machine learning results due to hidden data processing. Researchers must be aware of "off-label" data usage to avoid artificial improvements in algorithms.

Keywords:
MRIbiasbig datadata crimesinverse problem

Related Experiment Videos

Last Updated: Sep 29, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.6K

Area of Science:

  • Machine learning
  • Data science
  • Medical imaging

Background:

  • Public databases are crucial for machine learning (ML) research.
  • However, using data for unintended purposes (
  • off-label
  • usage) can skew results.
  • Hidden data processing pipelines in public datasets are a significant concern.

Purpose of the Study:

  • To investigate the impact of
  • off-label
  • data usage on ML algorithms.
  • To quantify the artificial performance improvements caused by hidden data processing.
  • To raise awareness about
  • data crimes
  • in big data research.

Main Methods:

  • Studied three established algorithms for magnetic resonance imaging (MRI) reconstruction.
  • Applied these algorithms to public databases.
  • Analyzed the resulting performance metrics for bias.

Main Results:

  • Off-label usage of public data led to biased, overly optimistic ML algorithm results.
  • Observed artificial performance improvements of up to 48% in MRI reconstruction.
  • Identified hidden processing pipelines as the cause of feature alteration.

Conclusions:

  • The
  • off-label
  • use of public data can significantly compromise the reliability of ML research.
  • Researchers must validate data processing pipelines when using public datasets.
  • This highlights a critical issue in big data research, termed
  • data crimes
  • .