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相关概念视频

Stereotype Content Model02:16

Stereotype Content Model

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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...
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Stereotype Threat and Self-fulfilling Prophecies02:09

Stereotype Threat and Self-fulfilling Prophecies

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When we hold a stereotype about a person, we have expectations that he or she will fulfill that stereotype. A self-fulfilling prophecy is an expectation held by a person that alters his or her behavior in a way that tends to make it true. When we hold stereotypes about a person, we tend to treat the person according to our expectations. This treatment can influence the person to act according to our stereotypic expectations, thus confirming our stereotypic beliefs. Research by Rosenthal and...
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Stereotypes, Prejudice, and Discrimination02:55

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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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Implicit Personality Theories01:23

Implicit Personality Theories

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Implicit personality theory explains how individuals make assumptions about the relationships between personality traits, behaviors, and character types. When people learn that someone possesses a particular trait, they tend to infer the presence of other related characteristics, forming a cohesive impression. This cognitive shortcut plays a crucial role in social interactions and interpersonal judgments.Central Traits and Their InfluenceSolomon Asch's seminal 1946 study highlighted the power...
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Motivational Bias01:25

Motivational Bias

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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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Language and Cognition01:27

Language and Cognition

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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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相关实验视频

Updated: Mar 6, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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大型语言模型中的道德刻板印象.

Aliah Zewail1, Alexandra Figueroa2, Jesse Graham3

  • 1Department of Psychological and Brain Sciences, University of Massachusetts Amherst, Amherst, MA 01003.

Proceedings of the National Academy of Sciences of the United States of America
|March 4, 2026
PubMed
概括
此摘要是机器生成的。

大型语言模型 (LLM) 难以准确估计全球道德价值观,经常刻板印象非西方人口. 这些模型对跨文化道德估计存在重大伦理和认识风险.

关键词:
在这里,我们可以看到AIAIAI.文化 文化 文化 文化大型语言模型.这是道德的道德.

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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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相关实验视频

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科学领域:

  • 计算社会科学 计算社会科学
  • 跨文化心理学 跨文化心理学
  • 人工智能伦理学 人工智能伦理学

背景情况:

  • 大型语言模型 (LLM) 越来越多地用于数据分析,但它们在代表各种人类价值观方面的准确性尚不清楚.
  • 了解跨文化道德价值观对于全球合作和道德AI发展至关重要.

研究的目的:

  • 评估LLMs在估计48个国家的人口道德价值观的准确性.
  • 将LLM产生的道德观念与六个关键道德价值观的大规模调查数据进行比较.

主要方法:

  • 在48个国家,LLM被要求估计"平均"人的道德规范.
  • 研究结果与实证调查数据对六个道德价值观进行了比较:关心,平等,比例,忠诚,权威和纯洁.

主要成果:

  • 在捕捉全球道德多样性方面,LLM的准确性很差,系统地歪曲道德价值观.
  • 模型高估了诸如关怀之类的价值,低估了诸如纯洁之类的价值.
  • 法律学士表现出偏见,高估了西方的道德问题,低估了非西方的道德问题,表明了刻板印象.

结论:

  • 在产生跨文化道德价值估计时,LLM是不可靠的.
  • 由于固有的偏见和不准确性,对此类任务的依赖LLM带来了重大的伦理和认识风险.
  • 这些发现强调了在敏感的跨文化研究中使用LLM时需要谨慎.