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"But with all of the successes of AI, it's also important to pay attention to when, and how, it can go wrong, in order to prevent future errors. A recent paper by Roman Yampolskiy, director of the Cybersecurity Lab at the University of Louisville, outlines a history of AI failures which are "directly related to the mistakes produced by the intelligence such systems are designed to exhibit." According to Yampolskiy, these types of failures can be attributed to mistakes during the learning phase or mistakes in the performance phase of the AI system."

"Concerns have been growing about AI's so-called "white guy problem" and now scientists have devised a way to test whether an algorithm is introducing gender or racial biases into decision-making."

"That can include statistical or machine learning algorithms that rely on police records detailing the time, location, and nature of past crimes in a bid to predict if, when, where, and who may commit future infractions. In theory, this should help authorities use resources more wisely and spend more time policing certain neighborhoods that they think will yield higher crime rates."