interpretml

Explain glass-box and black-box machine learning models with interactive visualizations.

2|Updated Jan 15, 2026
One-click install
npx skills add https://github.com/DTMC-marketplace/governance --skill interpretml
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: interpretml
Source: https://github.com/DTMC-marketplace/governance/tree/main/skills/interpretml
Command: npx skills add https://github.com/DTMC-marketplace/governance --skill interpretml

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides a unified framework for understanding and explaining machine learning models, crucial for compliance and trust in AI systems.

Core Features & Use Cases

  • Model Interpretability: Offers tools to explain both glass-box (EBM) and black-box models.
  • Compliance Assessment: Aids in evaluating AI systems against regulatory requirements like the EU AI Act's Article 13.
  • Risk Mitigation: Helps implement controls for trust-related risks in AI deployments.
  • Use Case: A data scientist needs to explain why a complex AI model made a specific prediction for a loan application to satisfy regulatory scrutiny.

Quick Start

Use the interpretml skill to assess compliance with Art. 13 requirements for the AI system.

Frequently Asked Questions about interpretml

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I explain machine learning model predictions for regulatory compliance?

You can explain machine learning model predictions for regulatory compliance by applying a unified interpretability framework that supports both glass-box and black-box models, generating interactive visualizations and mapping regulatory requirements to satisfy scrutiny.

What is the difference between glass-box and black-box model interpretability?

Glass-box model interpretability uses inherently transparent algorithms like EBMs, while black-box interpretability applies post-hoc explanation techniques to complex models. Both approaches provide interactive visualizations to understand machine learning behavior.

Can I assess EU AI Act Article 13 requirements for my AI system?

Yes, you can assess EU AI Act Article 13 requirements for your AI system using a framework that evaluates model explanations and maps them to regulatory contexts. This process aids in implementing controls for trust-related risks.

How do I mitigate trust-related risks in AI deployments?

Mitigate trust-related risks in AI deployments by using model interpretability tools to implement controls that explain predictions and evaluate compliance. This ensures AI systems meet transparency requirements and builds stakeholder confidence.

Does this interpretability framework work with black-box models?

Yes, the interpretability framework works with black-box models by providing tools that explain complex predictions without exposing internal algorithms. It generates interactive visualizations to help understand and trust model outputs.

When do I need machine learning interpretability for my AI system?

You need machine learning interpretability when deploying AI systems subject to regulatory scrutiny, such as loan applications, or when building trust with stakeholders. It is essential for assessing compliance with frameworks like the EU AI Act.