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Bias

Bias

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...

Strategies for Assessing and Addressing Confounding

Strategies for Assessing and Addressing Confounding

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...

Bias in Epidemiological Studies

Bias in Epidemiological Studies

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:

Confirmation Biases

Confirmation Biases

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?

Systematic Error: Methodological and Sampling Errors

Systematic Error: Methodological and Sampling Errors

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...

Hindsight Biases

Hindsight Biases

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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Related Experiment Video

Updated: May 15, 2025

Measuring Attentional Biases for Threat in Children and Adults

Measuring Attentional Biases for Threat in Children and Adults

Published on: October 19, 2014

Revisiting Technical Bias Mitigation Strategies.

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

Summary

This summary is machine-generated.

Technical solutions for artificial intelligence (AI) bias in healthcare face practical limits. This review analyzes these limitations and proposes value-sensitive AI to ensure fairness for diverse populations.

Keywords:

AI bias bias mitigation ethics stakeholder engagement value-sensitive design

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Last Updated: May 15, 2025

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Area of Science:

Computer Science
Medical Informatics
AI Ethics

Background:

Artificial intelligence (AI) bias mitigation predominantly relies on technical solutions.
Existing reviews often overlook the practical implementation challenges of these solutions in real-world healthcare settings.

Purpose of the Study:

To critically analyze the practical limitations of technical AI bias mitigation strategies in healthcare.
To identify key dimensions affecting the real-world implementation of AI fairness solutions.
To propose value-sensitive AI as a framework for stakeholder engagement and value embodiment.

Main Methods:

Structured analysis of AI bias mitigation limitations across five key dimensions: definition of bias/fairness, strategy selection, development stage, population applicability, and contextual design.
Illustration of limitations with empirical studies from healthcare and biomedical applications.
Discussion of value-sensitive AI framework and its application.

Main Results:

Technical AI bias mitigation strategies face significant practical limitations in healthcare.
Key challenges include defining bias/fairness, selecting compatible strategies, determining optimal implementation stages, ensuring population-specific applicability, and adapting to contextual nuances.
Value-sensitive AI offers a promising approach to address these limitations by integrating stakeholder values.

Conclusions:

Technical solutions alone are insufficient for mitigating AI bias in healthcare.
A deeper understanding of practical implementation challenges is crucial for developing effective AI fairness strategies.
Adopting value-sensitive AI principles can lead to more equitable and trustworthy AI systems in healthcare.