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Framing Effects03:26

Framing Effects

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Information is everywhere and its presentation—such as how and when items are presented—can impact our perceptions and decisions surrounding the info. This broad concept umbrellas framing effects—influences that occur due to the way information is framed in its appearance, whether it’s purely the order or the specific wording of a message. Let’s take a look at numerous ways in which two versions of something can objectively say the same thing, yet we respond in...
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Language and Cognition01:27

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Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.
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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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Motivational Bias01:25

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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,...
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Fundamental Attribution Error01:14

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According to some social psychologists, people tend to overemphasize internal factors as explanations—or attributions—for the behavior of other people. They tend to assume that the behavior of another person is a trait of that person, and to underestimate the power of the situation on the behavior of others. They tend to fail to recognize when the behavior of another is due to situational variables, and thus to the person’s state. This erroneous assumption is...
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Correspondence Bias01:17

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Correspondence bias, also referred to as the fundamental attribution error, describes the tendency to attribute another person’s behavior to internal characteristics rather than situational influences. This cognitive bias leads individuals to overlook external factors that may be influencing actions, thereby fostering potentially inaccurate assessments of others’ intentions and dispositions.Empirical Evidence for Correspondence BiasResearch has consistently demonstrated the...
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Related Experiment Video

Updated: Jan 12, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Source framing triggers systematic bias in large language models.

Federico Germani1, Giovanni Spitale1,2

  • 1Institute of Biomedical Ethics and History of Medicine, University of Zurich, Zurich, Switzerland.

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|November 7, 2025
PubMed
Summary
This summary is machine-generated.

Large language models (LLMs) show high agreement in text evaluation, but this consistency falters when statements are framed as originating from specific nationalities, revealing systematic bias in AI judgments.

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Area of Science:

  • Artificial Intelligence
  • Natural Language Processing
  • Computational Social Science

Background:

  • Large language models (LLMs) are increasingly utilized for text evaluation.
  • Concerns exist regarding the consistency, bias, and robustness of LLM judgments.
  • The impact of framing effects on LLM evaluations requires thorough investigation.

Purpose of the Study:

  • To assess inter- and intramodel agreement among state-of-the-art LLMs in text evaluation.
  • To investigate the influence of source attribution (LLM vs. human, nationality) on LLM judgments.
  • To identify potential biases in LLM evaluations stemming from framing effects.

Main Methods:

  • Evaluated 4800 narrative statements across 24 diverse topics using four advanced LLMs.
  • Conducted a total of 192,000 individual assessments.
  • Manipulated statement source attribution to human authors of specified nationalities and other LLMs.

Main Results:

  • High inter- and intramodel agreement was observed across LLMs for general topic evaluations.
  • Source attribution significantly disrupted LLM agreement, demonstrating susceptibility to framing effects.
  • Attributing statements to Chinese individuals systematically reduced agreement scores, particularly for the DeepSeek Reasoner model.

Conclusions:

  • LLMs exhibit systematic bias when evaluating text influenced by source framing.
  • The neutrality and fairness of LLM-mediated information systems are potentially compromised by these framing effects.
  • Further research is crucial to mitigate biases and ensure reliable LLM-based text evaluation.