AI Interpretability Method

An AI Interpretability Method is an analysis method that explains AI system decisions, model behaviors, or internal representations.

• AKA: Explainable AI Method, AI Transparency Technique, Model Interpretability Approach, XAI Method.
• Context:
  • It can typically reveal Decision-Making Processes of black-box models.
  • It can typically identify Feature Importance and causal relationships.
  • It can typically detect Model Biases and failure modes.
  • It can typically support AI Safety Research and alignment verifications.
  • It can often trade off between Interpretability and model performances.
  • It can often require Domain Expertise for result interpretations.
  • It can often enable Regulatory Compliance and audit requirements.
  • It can range from being a Local AI Interpretability Method to being a Global AI Interpretability Method, depending on its explanation scope.
  • It can range from being a Post-Hoc Interpretability Method to being an Intrinsic Interpretability Method, depending on its application timing.
  • It can range from being a Model-Agnostic Method to being a Model-Specific Method, depending on its architecture dependence.
  • It can range from being a Qualitative Interpretability Method to being a Quantitative Interpretability Method, depending on its output type.
  • ...
• Example:
  • Feature-Based Methods, such as:
    • SHAP (SHapley Additive exPlanations) computing feature contributions.
    • LIME (Local Interpretable Model-agnostic Explanations) approximating local behaviors.
    • Integrated Gradients attributing predictions to input features.
  • Internal Analysis Methods, such as:
    • Mechanistic Interpretability Technique understanding neural circuits.
    • Attention Visualization showing transformer focuss.
    • Activation Maximization revealing learned patterns.
  • Behavioral Methods, such as:
    • Counterfactual Explanation showing decision boundarys.
    • Concept Activation Vector identifying high-level concepts.
  • ...
• Counter-Example:
  • Performance Metric, which measures accuracy not explainability.
  • Model Architecture, which defines structure not interpretation.
  • Training Algorithm, which optimizes parameters not understanding.
  • Black-Box Testing, which evaluates output not reasoning.
• See: Explainable AI, AI Transparency, Mechanistic Interpretability Technique, Feature Attribution, Model Explanation, AI Safety, Neural Network Analysis, Decision Tree, Attention Mechanism, AI Audit.

---