Abstract
In this article we investigate origins of several cases of failure of Artificial Intelligence (AI) systems employing machine learning and deep learning. We focus on omission and commission errors in (a) the inputs to the AI system, (b) the processing logic, and (c) the outputs from the AI system. Our framework yields a set of 28 factors that can be used for reconstructing the path of AI failures and for determining corrective action. Our research helps identify emerging themes of inquiry necessary for developing more robust AI-ML systems. We are hopeful that our work will help strengthen the use of machine-learning AI by enhancing the rates of true positive and true negative judgements from AI systems, and by lowering the probabilities of false positive and false negative judgements.
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