Artificial intelligence (AI) is everywhere these days, from personal assistants to self-driving cars. With the continuous introduction of new technology, the use of artificial intelligence in business has been growing rapidly, and in ways that people have never imagined. AI is used in many software products and services, and is also being integrated into manufacturing processes. AI frequently automates routine tasks that were previously performed by people, eliminating tedious work, making business processes more efficient, and creating new capabilities and opportunities. Every business in the future will be using AI to some extent.

Although there are different ways to implement an AI system, the area with the most activity today is using machine learning, where the software learns and adapts. One type of machine learning is using a deep learning neural network. Deep learning neural networks mathematically simulate how neurons work in a human brain using linear algebra, and thus, these neural networks operate as a very simplistic human brain. They learn, and are not simply programmed as with most software. In order to learn, the neural network receives training information. The output of the neural network is compared to a desired result and then the complicated math within the neural network is adjusted to more closely achieve the desired result. After this training process is repeated a large number of times, the network is considered to be "trained" and it can predictably do the desired task. For example, many cellphones use facial recognition, to classify the features of a person's face. They learn the features of the cellphone owner's face and can use those features to recognize the owner with a high degree of accuracy. Because of this training phase of the AI system, the system is really only as good as the data set that is used to train the algorithm. If the training data is skewed, for example by being racially biased, the output of the AI system will be likewise biased. This is only one example of the way in which an AI algorithm can ultimately provide a biased output.