XAI - The eXplainable AI Platform [ALPHA]

Bringing together cutting edge research tools with the right processes to introduce explainability into machine learning systems.

The XAI library is open source, and converts the the Principles for Responsible Machine Learning into a practical set of tools and processes.

What do we mean by eXplainable AI?

We see the challenge of explainability as more than just an algorithmic challenge, which requires a combination of data science best practices with domain-specific knowledge. The XAI library is designed to empower machine learning engineers and relevant domain experts to analyse the end-to-end solution and identify discrepancies that may result in sub-optimal performance relative to the objectives required. More broadly, the XAI library is designed using the 3-steps of explainable machine learning, which involve 1) data analysis, 2) model evaluation, and 3) production monitoring.

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