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Related Concept Videos

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

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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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Avoidance-avoidance conflict refers to a psychological situation where a person must choose between two or more unpleasant alternatives. These conflicts are particularly stressful because neither option is desirable. This dilemma is often expressed in sayings like "caught between a rock and a hard place" or "between the devil and the deep blue sea." For instance, individuals who fear dental procedures may find themselves torn between enduring a painful toothache or facing the...
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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.
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Correspondence bias, also referred to as the fundamental attribution error, describes the tendency to attribute another person’s behavior to internal characteristics rather than situational influences. This cognitive bias leads individuals to overlook external factors that may be influencing actions, thereby fostering potentially inaccurate assessments of others’ intentions and dispositions.Empirical Evidence for Correspondence BiasResearch has consistently demonstrated the...
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Self-Serving Bias01:29

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Self-serving bias is a cognitive phenomenon in which individuals attribute positive outcomes to internal factors such as their abilities, intelligence, or effort while attributing negative outcomes to external circumstances. This cognitive distortion helps maintain self-esteem but can also impede objective self-assessment.Theoretical Explanations of Self-Serving BiasTwo primary theories explain the self-serving bias: the cognitive explanation and the motivational explanation.The cognitive...
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Development of New Therapeutic Applications Using Microfluidics
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Can AI developers avoid bias in public health applications?

Rebekah J Harms1,2, Rachel A Ankeny3, Lucy Carter2,4

  • 1Disability Innovation Institute, UNSW Sydney, Sydney, NSW, Australia.

Frontiers in Public Health
|January 29, 2026
PubMed
Summary
This summary is machine-generated.

AI-enabled personalized treatments offer health benefits but risk bias. Developers face practical challenges in mitigating this bias, impacting equitable treatment design and responsibility.

Keywords:
artificial intelligencebiasengineering biologypublic healthresponsibility

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Area of Science:

  • Engineering biology
  • Artificial intelligence
  • Biotechnology
  • Medical informatics

Background:

  • Advancements in AI and engineering biology enable personalized medical treatments.
  • Personalized treatments have significant public health implications but carry risks of bias.
  • Bias in AI-driven treatments can disproportionately harm specific subpopulations.

Purpose of the Study:

  • To highlight practical challenges faced by AI developers in mitigating bias in personalized treatments.
  • To examine the implications of these challenges for equitable treatment design.
  • To consider how limitations affect responsibility attribution for bias mitigation.

Main Methods:

  • Qualitative analysis of developer challenges in bias detection and removal.
  • Exploration of practical constraints within the AI development pipeline.
  • Discussion of ethical considerations and responsibility frameworks.

Main Results:

  • Existing bias mitigation strategies often overlook practical developer challenges.
  • Developers encounter significant hurdles in identifying and rectifying bias.
  • Acknowledging these limitations is crucial for fair responsibility assignment.

Conclusions:

  • Addressing practical challenges is essential for equitable AI-driven healthcare.
  • Clearer frameworks are needed to guide developers in bias mitigation.
  • Equitable development requires understanding and mitigating real-world constraints.