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