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

Stereotypes, Prejudice, and Discrimination02:55

Stereotypes, Prejudice, and Discrimination

91.1K
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...
91.1K
Confirmation Biases01:31

Confirmation Biases

6.6K
The confirmation bias is the tendency to focus on information that confirms our existing beliefs and ignore information that is inconsistent with our expectations. For example, if you think that your professor is not very nice, you notice all of the instances of rude behavior exhibited by the professor while ignoring the countless pleasant interactions he is involved in on a daily basis. Have you ever fallen prey to the confirmation bias, either as the source or target of such bias?
6.6K
Bias01:22

Bias

4.8K
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...
4.8K
Stereotype Content Model02:16

Stereotype Content Model

14.8K
The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
14.8K
The Representativeness Heuristic02:13

The Representativeness Heuristic

16.1K
The representative heuristic describes a biased way of thinking, in which you unintentionally stereotype someone or something. For example, you may assume that your professors spend their free time reading books and engaging in intellectual conversation, because the idea of them spending their time playing volleyball or visiting an amusement park does not fit in with your stereotypes of professors.
16.1K
X-linked Traits01:19

X-linked Traits

55.2K
In most mammalian species, females have two X sex chromosomes and males have an X and Y. As a result, mutations on the X chromosome in females may be masked by the presence of a normal allele on the second X. In contrast, a mutation on the X chromosome in males more often causes observable biological defects, as there is no normal X to compensate. Trait variations arising from mutations on the X chromosome are called “X-linked”.
55.2K

You might also read

Related Articles

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

Sort by
Same author

From hashtags to ballots: Conceptualizing political influencers and evaluating their impact on election outcomes.

PloS one·2025
Same author

The reliability of replications: a study in computational reproductions.

Royal Society open science·2025
Same author

Not so binary or generalizable: Brain sex differences with artificial neural networks.

Proceedings of the National Academy of Sciences of the United States of America·2025
Same author

Because the machine can discriminate: How machine learning serves and transforms biological explanations of human difference.

Big data & society·2023
Same author

Name-based demographic inference and the unequal distribution of misrecognition.

Nature human behaviour·2023
Same author

Observing many researchers using the same data and hypothesis reveals a hidden universe of uncertainty.

Proceedings of the National Academy of Sciences of the United States of America·2022

Related Experiment Video

Updated: Sep 2, 2025

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues
07:34

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues

Published on: June 3, 2013

17.5K

Diagnosing Gender Bias in Image Recognition Systems.

Carsten Schwemmer1, Carly Knight2, Emily D Bello-Pardo3

  • 1GESIS-Leibniz Institute for the Social Sciences, Cologne, Germany.

Socius : Sociological Research for a Dynamic World
|August 8, 2022
PubMed
Summary

Commercial image recognition systems exhibit gender bias, disproportionately labeling women

Keywords:
biascomputational social sciencegenderimage recognitionstereotypes

More Related Videos

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

43.0K
Generating Strictly Controlled Stimuli for Figure Recognition Experiments
05:39

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Published on: March 18, 2019

5.3K

Related Experiment Videos

Last Updated: Sep 2, 2025

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues
07:34

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues

Published on: June 3, 2013

17.5K
Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

43.0K
Generating Strictly Controlled Stimuli for Figure Recognition Experiments
05:39

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Published on: March 18, 2019

5.3K

Area of Science:

  • Computer Vision
  • Artificial Intelligence Ethics
  • Gender Studies

Background:

  • Image recognition systems promise scalable image analysis without expert input.
  • Machine learning models are known to produce biased outputs, necessitating investigation.
  • Gender bias in AI is a critical concern impacting fairness and representation.

Purpose of the Study:

  • To evaluate gender biases in commercial image recognition platforms.
  • To analyze bias using images of U.S. politicians from Congress and Twitter.
  • To understand the implications of encoded biases on women's visibility and stereotypes.

Main Methods:

  • Utilized photographs of U.S. members of Congress and their Twitter images.
  • Employed crowdsourced validation to assess image recognition system outputs.
  • Analyzed the types and frequency of labels generated for male and female politicians.

Main Results:

  • Commercial systems generated correct yet biased labels, selectively reporting subsets of true labels.
  • Images of women received three times more annotations related to physical appearance compared to men.
  • Women in images were recognized at substantially lower rates than men.

Conclusions:

  • Commercial image recognition systems exhibit significant gender bias.
  • Encoded biases negatively affect women's visibility and reinforce harmful gender stereotypes.
  • Biased AI systems limit the validity of insights derived from image data.