H2O.ai has been a pioneer in Explainable AI and Machine Learning Interpretability for several years. In addition to publishing several leading books on how to build more Responsible AI, H2O.ai has also developed leading methods in the industry such as K-Lime. This deep commitment to better machine learning has been built directly into our suite of products enabling data scientists and business users to better understand what their model is thinking.

Robust Post-Hoc Explainability Toolkit

H2O.ai has built one of the most extensive suite of capabilities for reviewing your machine learning models after they have been developed such as: Shapley Values, K-Lime, Surrogate Decision Trees, Reason Codes, Partial Dependency Plots, Disparate Impact Analysis, Exportable Rules Based Systems, and more.

AutoML with Cutting Edge White-Box Modeling Methods

Leading methods such as Explainable Neural Networks (XNNs), GA2Ms, Light GBM, and XGBoost enable users to automatically build more transparent models without sacrificing accuracy.

Pioneers in Explainable AI

H2O has published multiple books on leading Machine Learning Interpretability and Explainable AI methods in addition to countless articles and papers published in leading conferences and journals.

Download “Responsible Machine Learning”

Download “An Introduction to Machine Learning Interpretability”

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