Introducing Example-based Explanations, a first-of-its-kind service to help users improve model performance by refining their data. Learn more.

Explainable AI

Tools and frameworks to understand and interpret your machine learning models.

Understand AI output and build trust image

Understand AI output and build trust

Explainable AI is a set of tools and frameworks to help you understand and interpret predictions made by your machine learning models, natively integrated with a number of Google's products and services. With it, you can debug and improve model performance, and help others understand your models' behavior. You can also generate feature attributions for model predictions in AutoML Tables, BigQuery ML, and Vertex AI, and visually investigate model behavior using the What-If Tool.

What's new

Design interpretable and inclusive AI

Design interpretable and inclusive AI

Build interpretable and inclusive AI systems from the ground up with tools designed to help detect and resolve bias, drift, and other gaps in data and models. AI Explanations in AutoML Tables, Vertex AI Predictions, and Notebooks provide data scientists with the insight needed to improve datasets or model architecture and debug model performance. The What-If Tool lets you investigate model behavior at a glance.

Deploy AI with confidence image

Deploy AI with confidence

Grow end-user trust and improve transparency with human-interpretable explanations of machine learning models. When deploying a model on AutoML Tables or Vertex AI, you get a prediction and a score in real time indicating how much a factor affected the final result. While explanations don’t reveal any fundamental relationships in your data sample or population, they do reflect the patterns the model found in the data.

Streamline model governance

Streamline model governance

Simplify your organization’s ability to manage and improve machine learning models with streamlined performance monitoring and training. Easily monitor the predictions your models make on Vertex AI. The continuous evaluation feature lets you compare model predictions with ground truth labels to gain continual feedback and optimize model performance.


Understand AI output with groundbreaking XAI tools, developed by Google Research and used to power AI at Google.

Feature attributions

A managed service for generating feature attributions. Supported methods include Samples Shapely, Integrated Gradients, and XRAI.

Integrated into Vertex AI services, including AutoML Tables and Vision, Vertex AI Prediction, Notebooks, Model Monitoring, and BigQuery ML

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Example-based Explanations

Build better models with actionable explanations to mitigate data challenges.

A managed Approximate Nearest Neighbor service for returning similar examples to new predictions or instances.

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Model analysis

An advanced model analysis toolkit to help you better understand models.

Take action in Vertex AI to inspect models through an interactive dashboard with the integrated What-If Tool.

Alternatively, utilize open source with the What-If Tool or the Learning Interpretability Tool.

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Explainable AI tools are provided at no extra charge to users of AutoML Tables or Vertex AI. Note that Cloud AI is billed for node-hours usage, and running AI Explanations on model predictions will require compute and storage. Therefore, users of Explainable AI may see their node-hour usage increase.

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Tools and frameworks to deploy interpretable and inclusive machine learning models.

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