Explainable AI Agent

An Explainable AI Agent is an AI agent that provides transparent reasoning (to justify agent decisions through interpretable processes and clear explanations).

• Context:
  • It can (typically) explain Decision Processes through interpretable models.
  • It can (typically) provide Insights into decision factors.
  • It can (typically) generate Explanations for stakeholders.
  • It can (often) visualize Decision Paths through explanation tools.
  • It can (often) maintain Trust through transparency.
  • ...
  • It can range from being a Simple Decision Tree to being a Complex Interpretable System, depending on its model sophistication.
  • It can range from being a Post-hoc Explainer to being an Inherently Interpretable System, depending on its explanation approach.
  • ...
• Examples:
  • Interpretable Models, such as:
    • Decision Tree Systems for clear reasoning.
    • Linear Models with feature importance display.
  • Explanation Tools, such as:
    • LIME Systems for local interpretation.
    • SHAP Analysis for feature attribution.
  • ...
• Counter-Examples:
  • Black-Box AI Agents, which lack transparency.
  • Opaque Neural Networks, which hide decision processes.
  • Complex Models without explanation capability.
• See: Interpretable AI, Decision Tree, Explanation System, Transparent Model.

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