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Related Concept Videos

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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Bias in Epidemiological Studies01:29

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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:  
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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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Confirmation Biases01:31

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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

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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

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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.
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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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Bias in artificial intelligence algorithms and recommendations for mitigation.

Lama H Nazer1, Razan Zatarah1, Shai Waldrip2

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

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|June 22, 2023
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Summary
This summary is machine-generated.

Artificial intelligence (AI) in healthcare can worsen disparities if biases are not addressed. This review identifies bias sources in AI development and offers strategies to promote health equity.

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Area of Science:

  • Healthcare technology
  • Medical informatics
  • Health equity research

Background:

  • Artificial intelligence (AI) adoption in healthcare is accelerating, with potential to improve access and equity.
  • Concerns exist regarding AI algorithms perpetuating biases and exacerbating healthcare disparities.
  • Understanding bias sources in AI development is crucial for equitable implementation.

Purpose of the Study:

  • To identify potential sources of bias in healthcare AI algorithms across all development stages.
  • To discuss strategies for mitigating bias and disparities in AI healthcare applications.
  • To provide recommendations for developers and users to advance health equity.

Main Methods:

  • Systematic review of literature on AI bias in healthcare.
  • Analysis of bias at each stage: problem framing, data collection, preprocessing, development, validation, and implementation.
  • Development of a checklist with actionable recommendations.

Main Results:

  • Bias can be introduced at every step of the AI lifecycle, from data to deployment.
  • Social determinants of health can significantly influence AI algorithm outcomes.
  • Specific mitigation strategies are proposed for each identified bias source.

Conclusions:

  • Proactive identification and mitigation of bias are essential for equitable AI in healthcare.
  • A comprehensive checklist aids developers and users in reducing AI-induced disparities.
  • Addressing AI bias is critical for achieving health equity for all populations.