Glossary of Explainable AI (XAI) Terms

 

Glossary of Explainable AI (XAI) Terms

🔹 A

Algorithmic Bias
Systematic and repeatable errors in AI outputs caused by skewed data, flawed model assumptions, or societal inequities embedded in training data.

Attention Mechanism
A method used in neural networks (like Transformers) to focus on specific parts of input data, often visualized to show what the model “pays attention” to during predictions.

🔹 B

Black Box Model
A model whose inner workings are difficult or impossible for humans to interpret, often used to describe complex deep learning systems.

Bias-Variance Tradeoff
A fundamental machine learning concept explaining how overly simple models (high bias) or overly complex models (high variance) affect accuracy and interpretability.

🔹 C

Concept Activation Vector (CAV)
A technique for testing how sensitive a model is to a high-level human concept (like “striped texture” in images) rather than individual features.

Counterfactual Explanation
A type of explanation that describes what minimal changes to input would change a model’s prediction, e.g., “If your income was $5,000 higher, the loan would be approved.”

🔹 D

Data Drift
Changes in data distribution over time that can degrade model performance and explanations.

Decision Boundary
The line or surface separating classes in a classification model, often visualized in interpretable ML.

🔹 E

Explainability
The degree to which a model’s inner workings and decisions can be understood in human terms. A broader concept than interpretability.

Explainable AI (XAI)
The practice and study of making AI systems’ behavior understandable to humans, often through methods, tools, and frameworks for interpretability, fairness, and transparency.

🔹 F

Feature Importance
A ranking of which input variables most influence a model’s predictions.

Fairness in AI
The practice of detecting and mitigating biases so AI systems treat individuals and groups equitably.

🔹 G

Grad-CAM (Gradient-weighted Class Activation Mapping)
A technique that highlights image regions most important for a CNN’s prediction, helping visualize model reasoning.

Global vs. Local Explanations

  • Global: Summarize model behavior overall.

  • Local: Explain a single prediction or instance.

🔹 H

Human-in-the-Loop (HITL)
An approach where humans collaborate with AI to ensure transparency, correctness, and accountability in decision-making.

Hyperparameters
Configuration settings (not learned from data) that influence a model’s structure or training, such as learning rate or number of layers.

🔹 I

Interpretability
The ability to understand the cause of a decision in terms of model structure or features. Often more technical than “explainability.”

Interpretable Model
A model that is inherently understandable, like linear regression or decision trees.

🔹 L

LIME (Local Interpretable Model-Agnostic Explanations)
An algorithm that explains individual predictions by approximating a complex model with a simpler, interpretable one around the instance of interest.

Latent Space
A compressed representation of input data that neural networks use internally to make predictions.

🔹 M

Model-Agnostic Explanation
A method that can explain predictions regardless of the underlying model architecture, like LIME or SHAP.

Model Cards
Documentation describing a model’s intended use, performance, limitations, and ethical considerations.

🔹 O

Opacity
The degree to which an AI system’s inner logic is inaccessible to humans. High opacity often motivates XAI research.

Outlier
A data point significantly different from others, which can influence model predictions or explanations.

🔹 P

Partial Dependence Plot (PDP)
A visualization showing how a feature affects model predictions while holding other features constant.

Post-hoc Explainability
Explanations added after a model is trained, often through approximation or visualization techniques.

🔹 R

Rule-Based Explanation
An interpretable approach that expresses model decisions as human-readable “if-then” rules.

Responsible AI
A broader movement emphasizing transparency, fairness, accountability, and safety in AI deployment.

🔹 S

SHAP (SHapley Additive exPlanations)
A powerful method based on game theory that assigns each feature a “contribution value” to a model’s prediction.

Surrogate Model
A simpler, interpretable model trained to mimic a complex model’s behavior for explanation purposes.

🔹 T

Transparency
The extent to which the structure, training, and behavior of an AI system are open to inspection and interpretation.

Trust in AI
A user’s confidence in AI systems, influenced by explainability, fairness, and predictability.

🔹 V

Visualization Techniques
Methods for rendering a model’s decisions or internal states understandable through graphs, heatmaps, or dashboards.

🔹 X

XAI (Explainable Artificial Intelligence)
An emerging discipline at the intersection of machine learning, ethics, psychology, and human-computer interaction that studies methods to make AI reasoning comprehensible.

🔹 Z

Zero-Shot Learning
A technique where a model predicts classes it hasn’t been explicitly trained on, often requiring extra care in explanation.

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