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Stereotype Content Model02:16

Stereotype Content Model

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 categorization, a person will feel...
Natural and Artificial Concepts01:24

Natural and Artificial Concepts

In psychology, concepts can be divided into two categories: natural and artificial. Natural concepts are formed through direct or indirect experiences. For example, consider the concept of snow. If you live in a place with regular snowfall, such as Essex Junction, Vermont, you know snow through direct experiences. You’ve seen it fall, touched it, shoveled it, and played in it. You recognize its texture, appearance, and even its smell. In contrast, if you live on an island like Saint Vincent in...
Components of Language01:24

Components of Language

Language, whether spoken, signed, or written, consists of specific components: lexicon and grammar. The lexicon is the vocabulary of a language, comprising its words. Grammar is the set of rules used to convey meaning through the lexicon. For example, English grammar adds “-ed” to most verbs to indicate past tense. Words are formed by combining phonemes, which are the basic sound units of a language. Different languages have different sets of phonemes (e.g., “ah” vs. “eh”). Phonemes combine to...
Language Development01:22

Language Development

Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
The critical period for language acquisition suggests that the ability to acquire language is at its peak early in life. As people age, this proficiency decreases. Language development begins very...
Language and Cognition01:27

Language and Cognition

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.
Automatic Processing and Automatic Social Behavior01:28

Automatic Processing and Automatic Social Behavior

Automatic processing refers to the cognitive operations that occur without conscious intent or awareness, playing a fundamental role in shaping social cognition and behavior. These processes enable individuals to navigate complex social environments efficiently by relying on mental shortcuts and pre-existing knowledge structures known as schemas. One of the most influential mechanisms underlying automatic processing is priming, which subtly activates mental representations through exposure to...

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Updated: Jun 14, 2026

Experimental Paradigm for Measuring the Effect of Induced Emotion on Grammar Learning
05:33

Experimental Paradigm for Measuring the Effect of Induced Emotion on Grammar Learning

Published on: January 29, 2020

言語コーポラから自動的に派生する意味論は,人間のようなバイアスを含んでいます.

Aylin Caliskan1, Joanna J Bryson1,2, Arvind Narayanan1

  • 1Center for Information Technology Policy, Princeton University, Princeton, NJ, USA. aylinc@princeton.edu jjb@alum.mit.edu arvindn@cs.princeton.edu.

Science (New York, N.Y.)
|April 15, 2017
PubMed
まとめ
この要約は機械生成です。

ウェブテキストで訓練された機械学習モデルは,暗黙の関連テストで発見された人間の意味論的バイアスを複製します. これは歴史的なバイアスが 言語データに埋め込まれていることを明らかにし テクノロジーにおける 文化的バイアスを特定し 解決する方法を提示しています

さらに関連する動画

Decoding Natural Behavior from Neuroethological Embedding
08:00

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

関連する実験動画

Last Updated: Jun 14, 2026

Experimental Paradigm for Measuring the Effect of Induced Emotion on Grammar Learning
05:33

Experimental Paradigm for Measuring the Effect of Induced Emotion on Grammar Learning

Published on: January 29, 2020

Decoding Natural Behavior from Neuroethological Embedding
08:00

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

科学分野:

  • 人工知能
  • 自然言語処理
  • コンピュータ社会科学

背景:

  • 機械学習 (ML) は,データのパターンを特定することによって人工知能を導きます.
  • 人間の言語の体には 隠された社会的バイアスが含まれています
  • 暗黙の関連テスト (IAT) は,概念間の自動関連の強さを測定します.

研究 の 目的:

  • 人間の言語で訓練された機械学習モデルが 人間のような意味学的なバイアスを示すかどうかを調査する.
  • MLモデルがIATで測定されたバイアスを再現できるかどうかを判断する.
  • 文化的なバイアスを特定し緩和するMLの可能性を探求する.

主な方法:

  • 統計的な機械学習モデルを ワールドワイドウェブからの大量のテキストに適用した.
  • 標準のテキストデータでモデルを訓練した.
  • IATで測定したものを含め,既知の人間のバイアスに対してモデルの意味学的な関連性を評価した.

主要な成果:

  • MLモデルは人間の意味学的なバイアスのスペクトルを複製し,IATの結果を反映した.
  • バイアスは,道徳的に中立 (昆虫,花),問題 (人種,性別),真実 (性別,キャリア/名前) などの様々な領域で観察されました.
  • テキスト・コーポラは ML分析によって 回復可能な 歴史的な人間のバイアスを正確に 刻印します

結論:

  • リアルなテキストデータで訓練された機械学習モデルは 人間の意味学的なバイアスを継承し 反映しています
  • テキストデータは,MLを使用して定量化できる歴史的バイアスのリポジトリとして機能します.
  • 開発された方法は,文化的および技術的なバイアスを検出し対処するための有望なアプローチを提供します.