Gartner Research

Improve the Machine Learning Trust Equation by Using Explainable AI Frameworks

Published: 30 December 2019

ID: G00386498

Analyst(s): Sumit Agarwal


Organizations looking to harness the power of machine learning models are exposed to regulatory scrutiny and algorithm risk as they adopt ML-driven AI solutions for key business functions. This research provides data and analytics technical professionals with crucial elements of ML explainability.

Table Of Contents


  • Model Interpretability Use Cases
    • Meeting Regulatory Requirements
    • Explaining Customer Decisions
    • Model Debugging
  • Model Explainability Frameworks
    • Model-Agnostic Explainability
    • Model-Specific Explainability
  • Relationship Between Model Interpretability and Inference Accuracy
  • Products Supporting AI Explainability
    • DataRobot
    • Google Cloud Platform (GCP): AI Platform
    • H2O Driverless AI
    • IBM Watson OpenScale
    • Microsoft Azure
  • Strengths
  • Weaknesses


  • Dynamic Landscape Needs Proper Assessment
  • Align Explainability Tools and Frameworks With Use-Case and Impacted Personas
  • Not All Business Problems Require Explainability

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