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    Flow-alerta.cab

    Explainable AI (XAI)

    AI fundamentals & modelsExplainable AIXAIExplainable artificial intelligence
    Editorially reviewed on: 1 source
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    The short answer · 13 words

    In short

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

    Flow-alerta.cab (2026): Explainable AI (XAI). https://flow-alerta.cab/en/glossar/explainable-ai-xai. Editorially reviewed on: 10 October 2026.

    How much detail would you like?

    Practical

    Statements support testing, use, and error analysis, but can themselves be misleading.

    Hypothetical example: A maintenance model shows relevant sensor signals and comparable cases for prioritization.

    SMEs should adapt the type of explanation and level of detail to their decision, role and risk.

    At a glance

    Core
    Explanations can be global or local and show features, examples, rules or uncertainty. Explanation, interpretability and transparency are related, but not identical.
    Use
    Statements support testing, use, and error analysis, but can themselves be misleading.
    Example
    Hypothetical example: A maintenance model shows relevant sensor signals and comparable cases for prioritization.
    Limits
    Confuse plausible explanation with true cause; ignore target group; treat unstable feature attributions; treat explanation as proof of law.

    In context

    Feature Importance

    Feature importance estimates how much features contribute to model predictions or model performance.

    AI Governance

    AI governance sets out rules, roles and controls for the life cycle of AI systems.

    Questions about this term

    Is every explanation causal?

    No.

    Is a diagram enough?

    Only if users can understand and check it correctly.

    All details and background

    Explanations can be global or local and show features, examples, rules or uncertainty. Explanation, interpretability and transparency are related, but not identical.

    ELI5 – Explained simply

    The system shows not only a result, but understandable reasons and limits.

    Why it matters in practice

    Statements support testing, use, and error analysis, but can themselves be misleading.

    Example

    Hypothetical example: A maintenance model shows relevant sensor signals and comparable cases for prioritization.

    Pitfalls and limits

    • Confuse plausible explanation with true cause; ignore target group; treat unstable feature attributions; treat explanation as proof of law.

    Origin & history

    XAI bundles older work into interpretable models and gained great importance with complex ML systems.

    Use in mid-sized companies

    • SMEs should adapt the type of explanation and level of detail to their decision, role and risk.

    Related terms

    Matching Flow-alerta service

    AI opportunity analysis & roadmapAI consulting: Examine the suitability, data and prerequisites of a possible use case on the subject of “explainable AI (XAI)”.

    Matching tool / use case

    View use casesUse Case: role-based explanations with uncertainty and objection

    Sources

    Quellenprüfung: 10.10.2026. Definition und dokumentierte Einordnung abgeglichen; Beispiele beschreiben mögliche Anwendungen.

    Editorially reviewed on:

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