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" ... As a key reminder, the main goal of AI is to create an acceptable model for human understanding and interpretability The development of an AI leverages various branches of mathematical literacy. The first area that CEOs and Board Directors should learn about is on the underpinnings of Linear Algebra and appreciate the importance of Vectors and Probability Concepts. ... "
" ... Getting the most value from artificial intelligence (AI) investments requires a robust AI engineering strategy to driver scalability, better performance, reliability and interpretability of AI models. Many companies have had issues with AI projects due to governance, scalability and maintainability. ... "
" ... Transparency of algorithms will often (as we saw in the GDPR discussion) lead to interpretability questions. E.g., why did the algorithm reject my credit application? Explainable AI or XAI is a new field in research. Some machine learning algorithms such as decision trees, Bayesian classifiers, and linear models are easy to interpret since the data/features used are explainable by themselves. For example, an algorithm predicting the odds of being 'overweight' might use age, type of residence, education level, wealth index, etc... Those variables will be weighted during model training. The combination of weights and variables can explain later predictions. Deep learning models, on the other hand, can not be explained so easily. In Deep Learning, the algorithms create their own variables, which might not make immediate sense for us humans. ... "