Understanding AI Interpretability: Local vs. Global Perspectives

As Artificial Intelligence (AI) systems become more complex and integrated into critical decision-making processes, understanding why they make certain predictions is paramount. This field, known as AI interpretability or explainability, aims to make AI models transparent. A key distinction within this field is between 'local' and 'global' interpretability.

Local Interpretability: Explaining Individual Predictions

Local interpretability focuses on understanding the reasoning behind a single prediction made by an AI model. It answers the question: 'Why did the model make this specific decision for this particular input?' This is crucial for debugging, building trust with users, and ensuring fairness in individual cases.

Local interpretability explains one prediction at a time.

Imagine a loan application being denied. Local interpretability would highlight which specific factors (e.g., credit score, debt-to-income ratio) most influenced that denial for that applicant.

Techniques like LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations) are popular methods for achieving local interpretability. They work by approximating the complex model's behavior around a specific data point, providing insights into feature importance for that instance.

What is the primary goal of local interpretability?

To explain a single, specific prediction made by an AI model.

Global Interpretability: Understanding Overall Model Behavior

Global interpretability, on the other hand, aims to understand the model's behavior as a whole. It seeks to answer: 'How does the model generally make predictions?' or 'What are the most important features across all predictions?' This provides a broader understanding of the model's learned patterns and potential biases.

Global interpretability reveals the model's general decision-making logic.

Instead of looking at one loan denial, global interpretability might reveal that, across all loan applications, 'credit score' is consistently the most influential factor, regardless of the individual applicant.

Methods for global interpretability include analyzing feature importance across the entire dataset, examining decision trees (if applicable), or using techniques that summarize model behavior. This perspective is vital for model validation, regulatory compliance, and understanding systemic risks.

Feature	Local Interpretability	Global Interpretability
Focus	Single prediction	Overall model behavior
Question Answered	Why this prediction?	How does the model work generally?
Use Cases	Debugging, user trust, fairness for individuals	Model validation, regulatory compliance, systemic risk assessment
Common Techniques	LIME, SHAP	Feature importance (global), partial dependence plots, surrogate models

Think of local interpretability as understanding a single step in a complex dance, while global interpretability is understanding the entire choreography.

Why Both Are Important for AI Safety and Alignment

In AI safety and alignment engineering, both local and global interpretability are indispensable. Local explanations help ensure that individual decisions are fair and justifiable, preventing harm in specific instances. Global explanations provide assurance that the model's underlying logic is sound, aligned with human values, and not exhibiting unintended biases or behaviors at a systemic level. Together, they build robust, trustworthy, and safe AI systems.

Why are both local and global interpretability important for AI safety?

Local interpretability ensures fairness in individual decisions, while global interpretability ensures the overall model logic is sound and aligned with values.

Learning Resources

Explainable AI (XAI) - Google AI(documentation)

An overview of Google's efforts and research in Explainable AI, covering various techniques and their importance.

SHAP: Explaining Predictions of Complex Models(documentation)

Official documentation for the SHAP library, a powerful tool for understanding individual predictions.

LIME: Local Interpretable Model-agnostic Explanations(documentation)

The GitHub repository for LIME, providing code and examples for local model explanations.

Introduction to Explainable AI (XAI) - IBM(blog)

A comprehensive blog post explaining the concepts of XAI, including local and global interpretability, and their business implications.

Interpretable Machine Learning: A Guide for Making Black Box Models Explainable(documentation)

A free online book covering various interpretability methods, including detailed explanations of local and global techniques.

What is Explainable AI (XAI)? - Microsoft Azure(documentation)

Microsoft's perspective on XAI, detailing its importance and how it's implemented in their Azure AI services.

Towards a Global Understanding of Machine Learning Models(paper)

A research article exploring methods and challenges in achieving a global understanding of machine learning models.

SHAP Values Explained(video)

A video tutorial explaining the intuition and application of SHAP values for local interpretability.

AI Interpretability and Explainability - Stanford University(documentation)

Stanford's HAI initiative page on interpretability, linking to relevant research and resources.

Explainable AI (XAI) - Wikipedia(wikipedia)

A foundational Wikipedia article providing a broad overview of Explainable AI, its goals, and common approaches.

Local vs. Global Interpretability: Explaining individual predictions versus overall model behavior