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AI and machine learning have demonstrated some impressive abilities in recent years, but the models behind the technology and the reasons why it came to the decision it did are often hard for the people interacting with it to understand.

In order to help people gain an insight into machine decision making, IBM Research is launching AI Explainability 360, a comprehensive open source toolkit of state-of-the-art algorithms that support the interpretability and explainability of machine learning models.

AI Explainability 360 includes eight new algorithms developed by IBM Research, along with quantitative metrics to help measure explainability. It’s open source, so others can build on the knowledge and can learn from each other. Building on the successful release of the Adversarial Robustness 360 Toolbox (2018) and AI Fairness 360 Toolkit (2018), this is the latest from IBM Research, underscoring IBM’s commitment to trust and transparency.

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