Skip to content
    Flow-alerta.cab

    Algorithmic Discrimination

    Application & enablementAlgorithmic disadvantage
    Editorially reviewed on: 2 sources
    Your reading view

    The short answer · 13 words

    In short

    Systematic and unfair discrimination against certain groups of people through automated decision-making systems.

    Flow-alerta.cab (2026): Algorithmic Discrimination. https://flow-alerta.cab/en/glossar/algorithmic-discrimination. Editorially reviewed on: 09 October 2026.

    How much detail would you like?

    Practical

    Relevant to decisions about people; consider impacts and legal requirements before deployment.

    Possible example: A fictitious applicant model evaluates equally suitable applications differently depending on gender. The team examines data, characteristics and selection criteria.

    Examination of person-sensitive AI applications for disadvantages, for example in applicant selection or credit decision-making. Statistical tests do not replace a legal assessment.

    At a glance

    Core
    Automated systems can put people or groups at a disadvantage, for example through distorted data, unsuitable targets or features that indirectly map other properties. Whether a disadvantage is legally inadmissible depends on the use case and the applicable regulations.
    Use
    Relevant to decisions about people; consider impacts and legal requirements before deployment.
    Example
    Possible example: A fictitious applicant model evaluates equally suitable applications differently depending on gender. The team examines data, characteristics and selection criteria.
    Limits
    Believe algorithms are neutral just because they are mathematical; simply delete sensitive attributes without paying attention to proxy variables (such as residence).

    In context

    Disparate Impact

    Disparate Impact in U.S. law refers to adverse effects of seemingly neutral practices; it does not equate to a single fairness measure.

    EU AI Act

    The EU AI Act regulates AI systems risk-based and distributes duties among different actor roles.

    Explainable AI (XAI)

    XAI includes methods by which people can retrace AI results or model behavior.

    Questions about this term

    How can the risk be reduced?

    Through appropriate data, examination of results and indirect characteristics, as well as comprehensible control and complaint procedures; a single technique does not guarantee fairness.

    Is any statistical deviation unlawful?

    No. The legal framework, the comparative situation and possible justifications must be examined.

    All details and background

    Automated systems can put people or groups at a disadvantage, for example through distorted data, unsuitable targets or features that indirectly map other properties. Whether a disadvantage is legally inadmissible depends on the use case and the applicable regulations.

    ELI5 – Explained simply

    When a computer program treats people unnoticed because it has learned from old, flawed habits.

    Why it matters in practice

    Relevant to decisions about people; consider impacts and legal requirements before deployment.

    Example

    Possible example: A fictitious applicant model evaluates equally suitable applications differently depending on gender. The team examines data, characteristics and selection criteria.

    Pitfalls and limits

    • Believe algorithms are neutral just because they are mathematical; simply delete sensitive attributes without paying attention to proxy variables (such as residence).

    Origin & history

    Discrimination through selection procedures has already been legally treated before modern AI systems. Today, research and regulatory authorities are also investigating automated decisions; a single secured time of origin is not claimed here.

    Use in mid-sized companies

    • Examination of person-sensitive AI applications for disadvantages, for example in applicant selection or credit decision-making. Statistical tests do not replace a legal assessment.

    Related terms

    Matching Flow-alerta service

    AI adoption & supportAI Potential Analysis & Roadmap; AI Introduction & Accompanying – Identifying Risks and Additional Test Need

    Matching tool / use case

    View use casesUse Case: Compare the results of a decision-making system according to relevant groups and examine them technically.

    Sources

    Quellen geprüft am 09.10.2026.

    Editorially reviewed on:

    Next step

    Put knowledge into practice

    Discuss your projectView use cases