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Related Concept Videos

Bias01:22

Bias

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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...
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Group Polarization01:01

Group Polarization

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Group polarization is the strengthening of an original group attitude following the discussion of views within a group (Teger & Pruitt, 1967). That is, if a group initially favors a viewpoint, after discussion the group consensus is likely a stronger endorsement of the viewpoint. Conversely, if the group was initially opposed to a viewpoint, group discussion would likely lead to stronger opposition.
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Confirmation Biases01:31

Confirmation Biases

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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?
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Stereotypes, Prejudice, and Discrimination02:55

Stereotypes, Prejudice, and Discrimination

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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...
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The Representativeness Heuristic02:13

The Representativeness Heuristic

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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.
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The Anchoring-and-Adjustment Heuristic01:25

The Anchoring-and-Adjustment Heuristic

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In order to make good decisions, we use our knowledge and our reasoning. Often, this knowledge and reasoning is sound and solid. However, sometimes, we are swayed by biases or by others manipulating a situation. For example, let’s say you and three friends wanted to rent a house and had a combined target budget of $1,600. The realtor shows you only very run-down houses for $1,600 and then shows you a very nice house for $2,000. Might you ask each person to pay more in rent to get the...
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Related Experiment Video

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YouTube's recommendation algorithm is left-leaning in the United States.

Hazem Ibrahim1, Nouar AlDahoul1, Sangjin Lee1

  • 1Department of Computer Science, New York University Abu Dhabi, Abu Dhabi 129188, UAE.

PNAS Nexus
|August 21, 2023
PubMed
Summary

YouTube

Keywords:
algorithmic biaspolitical radicalizationrecommendation systems

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Area of Science:

  • Online political communication
  • Algorithmic bias in social media
  • Computational social science

Background:

  • YouTube is a major platform for online political video consumption.
  • Its recommendation algorithm significantly influences user exposure to content.
  • Concerns exist about algorithmic echo chambers and extremist content delivery.

Purpose of the Study:

  • To investigate potential political leanings of the YouTube recommendation algorithm.
  • To determine if the algorithm exhibits bias toward the Left or Right.
  • To understand the implications of algorithmic political bias on users.

Main Methods:

  • Constructed six archetypal user personas across the US political spectrum (Far Left to Far Right).
  • Conducted a controlled experiment over eight months, analyzing over 120,000 recommended videos.
  • Assessed the algorithm's impact on user consumption patterns and content exposure.

Main Results:

  • The algorithm exhibited an asymmetric pull away from political extremes, stronger for the Far Right than the Far Left.
  • YouTube's recommendations showed a leftward skew, even for users without prior watch history.
  • Algorithmic bias was observed, influencing content exposure across different political leanings.

Conclusions:

  • YouTube's recommendation algorithm demonstrates a political bias, skewing leftward.
  • This bias is asymmetric, impacting users differently based on their political orientation.
  • The findings raise critical questions about the societal and political implications of biased algorithms on platforms like YouTube.