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相关概念视频

Bias01:22

Bias

3.7K
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...
3.7K
Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

70
Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
70
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

101
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:  
101
Confirmation Biases01:31

Confirmation Biases

5.4K
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?
5.4K
Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

1.4K
In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
1.4K
Hindsight Biases01:12

Hindsight Biases

3.4K
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? 
3.4K

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相关实验视频

Updated: May 15, 2025

Measuring Attentional Biases for Threat in Children and Adults
08:25

Measuring Attentional Biases for Threat in Children and Adults

Published on: October 19, 2014

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重新审视技术偏差缓解策略

Abdoul Jalil Djiberou Mahamadou1, Artem A Trotsyuk1

  • 1Center for Biomedical Ethics, Stanford University School of Medicine, Stanford, California, USA; email: abdjiber@stanford.edu, atrotsyuk@stanford.edu.

Annual review of biomedical data science
|April 8, 2025
PubMed
概括

在医疗保健中对人工智能 (AI) 偏差的技术解决方案面临实际限制. 本综述分析了这些局限性,并提出了价值敏感的人工智能,以确保对不同人群的公平.

科学领域:

  • 计算机科学 计算机科学
  • 医疗信息学 医疗信息学
  • 人工智能伦理学

背景情况:

  • 人工智能 (AI) 偏差缓解主要依赖于技术解决方案.
  • 现有的审查往往忽视了这些解决方案在现实世界医疗保健环境中的实际实施挑战.

研究的目的:

  • 批判性地分析技术AI偏差缓解策略在医疗保健中的实际局限性.
  • 确定影响人工智能公平解决方案在现实世界中实施的关键维度.
  • 提出价值敏感的人工智能作为利益相关者参与和价值体现的框架.

主要方法:

  • 在五个关键方面对AI偏见缓解局限性的结构化分析:偏见/公平的定义,战略选择,开发阶段,人口适用性和上下文设计.
  • 用从医疗保健和生物医学应用中的经验研究来说明局限性.
  • 讨论价值敏感的人工智能框架及其应用.

主要成果:

  • 技术AI偏差缓解策略在医疗保健中面临重大实际限制.
  • 关键的挑战包括定义偏见/公平性,选择兼容的策略,确定最佳的实施阶段,确保特定人口的适用性,并适应上下文细微差别.
  • 价值敏感的人工智能提供了一个有希望的方法,通过整合利益相关者的价值来解决这些局限性.

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Movement Retraining using Real-time Feedback of Performance
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Movement Retraining using Real-time Feedback of Performance

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Assessment of Mouse Judgment Bias through an Olfactory Digging Task

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相关实验视频

Last Updated: May 15, 2025

Measuring Attentional Biases for Threat in Children and Adults
08:25

Measuring Attentional Biases for Threat in Children and Adults

Published on: October 19, 2014

15.2K
Movement Retraining using Real-time Feedback of Performance
08:16

Movement Retraining using Real-time Feedback of Performance

Published on: January 17, 2013

13.2K
Assessment of Mouse Judgment Bias through an Olfactory Digging Task
12:10

Assessment of Mouse Judgment Bias through an Olfactory Digging Task

Published on: March 4, 2022

2.5K

结论:

  • 仅靠技术解决方案就不足以缓解医疗保健中的AI偏见.
  • 对实际实施挑战的更深入的理解对于开发有效的AI公平战略至关重要.
  • 采用价值敏感的人工智能原则可以使医疗保健中的人工智能系统更公平,更值得信赖.