AI Explainability 360 help You Understand What Machine Learning

AI Explainability 360 help You Understand What Machine Learning

AI Explainability 360 Is an Open Source Toolkit to Help You Understand What Machine Learning. Models Are Doing A new toolkit for ML interpretability.

Interpretability is one of the most difficult challenges in modern machine learning solutions. While building sophisticated machine learning models is getting easier, understanding how models develop knowledge and arrive to conclusions remains a very difficult challenge. Typically, the more accurate the models the harder they are to interpret. Recently, AI researchers from IBM open sourced AI Explainability 360, a new toolkit of state-of-the-art algorithms that support the interpretability and explainability of machine learning models.

The release of AI Explainability 360 is the first practical implementation of ideas outlined in dozens of research papers in the last few years. In the same way traditional software applications incorporate instrumentation code to help its runtime monitoring, machine learning models need to add interpretability techniques to facilitate debugging, troubleshooting and versioning. However, interpreting machine learning models is a multiple of complexity higher than traditional software applications. For starters, we need have very little understanding of what makes a machine learning model interpretable.

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