AI Perspectives - Archive Alive

Interpretability vs Explainability

Understanding the difference in AI/ML systems

Interpretability

The degree to which a human can understand the cause of a decision made by a machine learning model. It's about understanding how the model works internally.

Characteristics

Focuses on model transparency and internal logic
Built into the model's architecture
Often requires simpler models (linear regression, decision trees)
Inherent to the model design
Answers: "How does it work?"

Visual Examples

Decision Tree

You can trace each decision path from root to leaf, seeing exactly which features led to the final prediction.

Linear Regression Coefficients

Each coefficient directly shows how much each feature contributes to the prediction.

Age:

+0.7

Income:

+0.9

Debt:

-0.4

When to Use

Regulated industries (healthcare, finance)
When trust is critical
When you need to debug model behavior
Simple, transparent decision-making required

Explainability

The ability to explain or describe the model's behavior in human terms. It's about communicating why a specific decision was made, regardless of internal complexity.

Characteristics

Focuses on post-hoc explanations of decisions
Can be applied to any model (even black boxes)
Uses external techniques (SHAP, LIME, attention maps)
Added after model is trained
Answers: "Why did it make this decision?"

Visual Examples

SHAP Values (Feature Importance)

Shows which features contributed most to a neural network's prediction, even though the network itself is a black box.

Prediction: Loan Approved (Confidence: 87%)

Income

+0.42

Credit Score

+0.31

Employment

+0.18

Debt Ratio

-0.15

Attention Visualization

In transformers, highlights which words the model focused on when making a prediction.

Sentiment: Positive

The amazing product exceeded my expectations

Darker highlight = higher attention weight

When to Use

Complex models (deep learning, ensembles)
When high accuracy is priority
Need to justify individual predictions
Building user trust without sacrificing performance

The Key Distinction

Interpretable models are transparent by design — you understand how they work internally. Explainable models can be black boxes, but you can explain specific decisions they make. Think of interpretability as "understanding the machine" and explainability as "understanding the decision."

Try It Yourself

Visit the Screenshot Gallery and click on any image to start an AI Agent Discussion. You can toggle between Interpretable and Explainable modes using the button next to the discussion header.