Interpretability

TL;DR

• Helps users understand why a model made a particular prediction so decisions can be trusted and acted on.
• Important in domains like healthcare and finance where explanations support clinical judgment, risk policies, and regulatory requirements.
• Can be achieved via inherently interpretable algorithms (e.g., decision trees, linear regression) or model-agnostic techniques (e.g., feature importance, model-agnostic explanations).

Definition

Interpretability refers to the ability to understand and explain the reasoning behind the decisions made by a model or algorithm.

Explanation

Interpretability is a crucial aspect of decision-making processes that use artificial intelligence and machine learning. It enables practitioners to examine a model’s reasoning, determine whether predictions align with domain expertise and policies, and communicate those reasons to affected parties. Interpretability supports accuracy, fairness, transparency, trust, and compliance with legal and ethical standards.

Approaches to improving interpretability include:

• Using interpretable algorithms such as decision trees and linear regression, which are designed to provide clear explanations of their predictions.
• Applying interpretability techniques like feature importance and model-agnostic explanations to reveal which factors most influenced a model’s decisions, including for more complex models.

Examples

Healthcare

When doctors diagnose a patient, they rely on data such as medical history, symptoms, and test results to make an informed decision about the patient’s health and treatment plan. A machine learning model may be used to analyze that data and make predictions about the patient’s condition. The model’s decisions need to be interpretable so doctors can understand why it made certain predictions and whether those predictions align with their clinical expertise.

Credit scoring

A financial institution evaluating a loan application uses data points such as the applicant’s credit history and income to determine creditworthiness. A machine learning model may predict the likelihood of the applicant defaulting on their loan. The model’s decisions need to be interpretable so the institution can understand why it made certain predictions, ensure they align with risk assessment policies, and explain decisions to applicants.

Use cases

• Healthcare diagnosis and treatment decision support
• Credit scoring and loan application assessment

Notes or pitfalls

• Interpretability is important for meeting legal and ethical requirements, such as providing explanations of diagnoses in healthcare or reasons for loan denials in credit decisions.
• Relying on uninterpretable models can hinder trust, make it difficult to detect unfairness or errors, and complicate compliance with regulations.

Related terms

• Decision trees
• Linear regression
• Feature importance
• Model-agnostic explanations