Advances in AI, especially based on machine learning, have provided a powerful way to extract useful patterns from large, heterogeneous data sources. The rise in massive amounts of data, coupled with powerful computing capabilities, makes it possible to tackle previously intractable real-world problems. Medicine, business, government, and science are rapidly automating decisions and processes using machine learning. Unlike traditional AI approaches based on explicit rules expressing domain knowledge, machine learning often lacks explicit human-understandable specification of the rules producing model outputs. With growing reliance on automated decisions, an overriding concern is understanding the process by which "black box" AI techniques make decisions. This is known as the problem of explainable AI.2 However, opening the black box may lead to unexpected consequences, as when opening Pandora's Box.
Advanced machine learning algorithms, such as deep learning neural networks or support vector machines, are not easily understood by humans. Their power and success stems from the ability to generate highly complex decision models built upon hundreds of iterations over training data.5 The performance of these models is dependent on many factors, including the availability and quality of training data and skills and domain expertise of data scientists. The complexity of machine learning models may be so great that even data scientists struggle to understand the underlying algorithms. For example, deep learning was used in the program that famously beat the reigning Go world champion,6 yet the data scientists responsible could not always understand how or why the algorithms performed as they did.
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