IBM offers explainable AI toolkit, but it's open to interpretation ZDNet

Decades before today's deep learning neural networks compiled imponderable layers of statistics into working machines, researchers were trying to figure out how one explains statistical findings to a human. IBM this week offered up the latest effort in that long quest to interpret, explain, and justify machine learning, a set of open-source programming resources it calls "AI 360 Explainability." It remains to be seen whether yet another tool will solve the conundrum of how people can understand what is going on when artificial intelligence makes a prediction based on data. The toolkit consists of eight different algorithms released in the course of 2018. The IBM tools are posted on Github as a Python library.