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相关概念视频

Bias01:22

Bias

4.3K
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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Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

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Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:  
375
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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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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Hindsight Biases01:12

Hindsight Biases

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Hindsight bias leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did. Can you relate this to the phrase "Hindsight is 20/20" now? 
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Types of Errors: Detection and Minimization01:12

Types of Errors: Detection and Minimization

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Error is the deviation of the obtained result from the true, expected value or the estimated central value. Errors are expressed in absolute or relative terms.
Absolute error in a measurement is the numerical difference from the true or central value. Relative error is the ratio between absolute error and the true or central value, expressed as a percentage.
Errors can be classified by source, magnitude, and sign. There are three types of errors: systematic, random, and gross.
Systematic or...
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相关实验视频

Updated: Jul 26, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

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人工智能算法中的偏差以及缓解偏差的建议.

Lama H Nazer1, Razan Zatarah1, Shai Waldrip2

  • 1Department of Pharmacy, King Hussein Cancer Center, Amman, Jordan.

PLOS digital health
|June 22, 2023
PubMed
概括

医疗保健中的人工智能 (AI) 如果不解决偏见,可能会加剧差异. 本综述确定了人工智能开发中的偏见来源,并提供了促进健康公平的策略.

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Last Updated: Jul 26, 2025

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科学领域:

  • 医疗保健技术 医疗保健技术 医疗保健技术
  • 医疗信息学医学信息学
  • 卫生公平研究 卫生公平研究

背景情况:

  • 人工智能 (AI) 在医疗保健中的采用正在加速,有可能改善获取和公平.
  • 人们担心人工智能算法会延续偏见并加剧医疗保健差异.
  • 了解人工智能开发中的偏见来源对于公平实施至关重要.

研究的目的:

  • 在所有开发阶段识别医疗保健AI算法的潜在偏差来源.
  • 讨论减轻人工智能医疗应用中的偏见和差异的策略.
  • 为开发人员和用户提供建议,以推进健康公平.

主要方法:

  • 对医疗保健中人工智能偏见的文献进行系统审查.
  • 在每个阶段对偏差的分析:问题框架,数据收集,预处理,开发,验证和实施.
  • 制定一个包含可执行建议的检查清单.

主要成果:

  • 偏差可以在AI生命周期的每个步骤中引入,从数据到部署.
  • 健康的社会决定因素可以显著影响人工智能算法结果.
  • 针对每一个已识别的偏差源,都提出了具体的缓解策略.

结论:

  • 积极识别和减轻偏见对于医疗保健中的公平人工智能至关重要.
  • 一个全面的检查清单有助于开发人员和用户减少人工智能引起的差异.
  • 解决人工智能偏见对于实现所有人口的健康平等至关重要.