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Explain, debug & audit
black-box machine learning models


Build trustworthy, transparent and unbiased AI solutions with explainX's open-source explainable AI python library
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Start building with explainX's API for free.
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Why explainX?

ExplainX helps data scientists, AI researchers and business stakeholders in any organization to gain a deep understanding of their machine learning models. It can also be used to debug models, explain predictions, monitor performance and enable auditing to meet compliance with regulatory requirements - all with a single line of code.

All black-box models supported

Support for multiple types of models and algorithms, during training and inferencing

Built for scale & production ML

APIs automatically scale to handle production workloads.

Open Source for Data Scientists

explainX is also open source and freely available for you to use today.

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Understand AI Predictions

Uncover the "why" and "how" behind each prediction made by the machine learning algorithm

Identify features that impact the prediction and optimize them.

Generate similar profiles to identify similar behaving clusters.

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Improve your
model accuracy

Debug your machine learning models and identify biases to improve model accuracy.

Explore the relationship between different variables to improve feature selection.

Drill down to monitor model performance in different subsets to address biases.

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Accelerate AI Compliance

Identify similar profiles to accelerate compliance and audit

Make customers & business stakeholders happier with responsible and explainable AI solutions

Reduce compliance costs by identifying biases and increasing AI understanding.

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Global
Explanation

Explain overall model behavior and identify biases in your data

Local 
Explanation

Explain the logic of each prediction in real-time

Scenario
Analysis

Play around with input feature values to see the potential impact on predictions

Prototypical Analysis

Support your analysis by finding similar profiles and counterfactuals

Analyze Model on Multiple Levels

Explore your data, model and rationale behind AI predictions in depth to gain a comprehensive view of your AI model.

Read Documentation
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Easy custom models integration

Explain your custom models. No matter it is XGBoost, CatBoost, Neural Network or a simple Logistic Regression, we have you covered.

We have built explainX using state-of-the-art techniques including SHAPLEY values, Integrated Gradients, What-If Analysis, in-house developed Contrastive Explanations and Prototypical Analysis

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Resources

Visit our Resources Section

Build explainable, trustworthy & unbiased AI solutions with explainX