How to achieve explainability in AI models

Traditional rule-based AI systems included explainability in AI as part of models, since humans would typically handcraft the inputs to output. But deep learning techniques using semi-autonomous neural-network models can't provide a model's results map to an intended goal. Researchers are working to build learning algorithms that generate explainable AI systems from data. Currently, however, most of the dominant learning algorithms do not yield interpretable AI systems, said Ankur Taly, head of data science at Fiddler Labs, an explainable AI tools provider. "This results in black box ML techniques, which may generate accurate AI systems, but it's harder to trust them since we don't know how these systems' outputs are generated," he said.