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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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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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Cause and Effect01:53

Cause and Effect

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While variables are sometimes correlated because one does cause the other, it could also be that some other factor, a confounding variable, is actually causing the systematic movement in our variables of interest. For instance, as sales in ice cream increase, so does the overall rate of crime. Is it possible that indulging in your favorite flavor of ice cream could send you on a crime spree? Or, after committing crime do you think you might decide to treat yourself to a cone?
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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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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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Fundamental Attribution Error01:14

Fundamental Attribution Error

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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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相关实验视频

Updated: May 15, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
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在基于AI的预测模型中导航公平性:理论构造和实际应用.

S L van der Meijden1,2, Y Wang3, M S Arbous1

  • 1Department of Intensive Care Medicine, The Leiden University Medical Center, Leiden, The Netherlands.

medRxiv : the preprint server for health sciences
|April 8, 2025
PubMed
概括
此摘要是机器生成的。

确保人工智能 (AI) 医疗保健模型的公平性对于公平的结果至关重要. 这项研究确定了关键的公平度指标,如临床效用和统计均等性,用于在医学中实际实施人工智能.

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Using Visual and Narrative Methods to Achieve Fair Process in Clinical Care
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Using Visual and Narrative Methods to Achieve Fair Process in Clinical Care

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Characterization of the Sense of Agency over the Actions of Neural-machine Interface-operated Prostheses
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Characterization of the Sense of Agency over the Actions of Neural-machine Interface-operated Prostheses

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相关实验视频

Last Updated: May 15, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
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Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

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Using Visual and Narrative Methods to Achieve Fair Process in Clinical Care
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Using Visual and Narrative Methods to Achieve Fair Process in Clinical Care

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科学领域:

  • 医疗信息学 医疗信息学
  • 人工智能伦理学 人工智能伦理学
  • 医疗人工智能 医疗人工智能

背景情况:

  • 人工智能 (AI) 预测模型越来越多地用于医疗保健.
  • 确保人工智能公平对抗健康差异和实现公平的患者结果至关重要.
  • 对公平的相互矛盾的定义对实际AI实施构成挑战.

研究的目的:

  • 构建人工智能公平性从理论到实践的过渡.
  • 为医疗人工智能应用确定适当的公平性指标.
  • 评估公平性定义,预期使用,决策类型和分配正义之间的关系.

主要方法:

  • 从最近的文献中审查了27个AI公平性的定义.
  • 评估每个定义与预期使用,决策影响和道德原则的关系.
  • 评估的临床效用,性能指标 (AUC),校准和医疗应用的统计平价.
  • 通过两个用例证明了适用性.

主要成果:

  • 临床实用性,基于绩效的指标 (AUC),校准和统计均等性被推为医疗AI最相关的基于群体的公平性指标.
  • 根据具体的预期用途和道德框架,可以应用不同的公平度指标.
  • 该研究为评估AI公平性和偏见缓解提供了基础.

结论:

  • 为了实现公平的医疗保健实施,需要对AI公平性指标采取结构化的方法.
  • 选择适当的公平度指标取决于临床实用性和伦理考虑的上下文.
  • 这项工作促进了更公平的AI开发和部署在医疗保健.