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    Algorithmic Bias and Fairness

    Systematic differences in model performance across demographic or clinical subgroups.

    Reviewed by Christian Espinosa, Founder, Blue Goat CyberLast reviewed May 5, 2026

    Definition

    Algorithmic bias occurs when a model performs differently across subgroups (e.g., by sex, race, age, comorbidity) due to training data imbalance, label noise, or proxy variables. Fairness analyses quantify and mitigate these gaps.

    What this means in practice

    FDA's GMLP principles call for representative datasets and subgroup performance analysis. EU AI Act high-risk requirements explicitly address bias monitoring across the lifecycle.

    Cross-references

    Part of

    A larger framework or document this term belongs to.

    Primary references

    3 sources
    Link health: 3 verified· last checked 2026-05-09
    FDA/Health Canada/MHRA·1IMDRF·1MDCG·1
    1. 1
      Good Machine Learning Practice
      Verified
      FDA/Health Canada/MHRAfda.gov
    2. 2
      IMDRF - Software as a Medical Device
      Verified
      IMDRFimdrf.org
    3. 3
      MDCG Software Guidance
      Verified
      MDCGhealth.ec.europa.eu

    Inline markers like [1] jump to the matching reference above.