The Essence of Explainable AI: Interpretability

SAN FRANCISCO – Applications of Artificial Intelligence, machine learning, and deep learning are relatively useless without a lucid understanding of how the outputs of their predictive models are derived. Explainable AI hinges on explainability--a clear verbalizing of how the various weights and measures of machine learning models generate their outputs. Those explanations, in turn, are determined by interpretability: the statistical or mathematical understanding of the numerical outputs of decisions made by predictive models. Interpretability is foundational to unraveling some of the more consistent issues plaguing AI today. Facilitating interpretability--and using it as the impetus for refining machine learning models and the data on which they're trained--is indispensable for overcoming the threat of biased models once and for all.