Explainability or interpretability of AI is a huge deal these days, especially due to the rise in the number of enterprises depending on the decisions made by machine learning and deep learning. Naturally, stakeholders want a level of transparency for how the algorithms came up with their recommendations. The so-called "black box" of AI is rapidly being questioned. For this reason, I was encouraged to learn of IBM's recent efforts in this area. The company's research arm just launched a new open-source AI toolkit, "AI Explainability 360," consisting of state-of-the-art algorithms that support the interpretability and explainability of machine learning models.

IBM Research today introduced AI Explainability 360, an open source collection of state-of-the-art algorithms that use a range of techniques to explain AI model decision-making. The launch follows IBM's release a year ago of AI Fairness 360 for the detection and mitigation of bias in AI models. IBM is sharing its latest toolkit in order to increase trust and verification of artificial intelligence and help businesses that must comply with regulations to use AI, IBM Research fellow and responsible AI lead Saska Mojsilovic told VentureBeat in a phone interview. "That's fundamentally important, because we know people in organizations will not use or deploy AI technologies unless they really trust their decisions. And because we create infrastructure for a good part of this world, it is fundamentally important for us -- not because of our own internal deployments of AI or products that we might have in this space, but it's fundamentally important to create these capabilities because our clients and the world will leverage them," she said.