With machine learning models, explainability is difficult and elusive

The push by enterprises for explainable artificial intelligence is shining a light on one of the problematic aspects of machine learning models. That is, if the models operate in so-called black boxes, they don't give a business visibility into why they've arrived at the recommendations they do. But, according to experts, the enterprise demand for explainable artificial intelligence overlooks a number of characteristics about current applications of AI, including the fact that not all machine learning models require the same level of interpretability. "The importance of interpretability really depends on the downstream application," said Zoubin Ghahramani, professor of information engineering at the University of Cambridge and chief scientist at Uber Technologies Inc., during a press conference at the recent Artificial Intelligence Conference hosted by O'Reilly Media and Intel AI. A machine learning model that automatically captions an image would not need to be held to the same standards as machine learning models that determine how loans should be distributed, he contended.