At a time when the value of data is never higher, many companies are investing in "artificial intelligence," in one or the other form, to help them transform business processes and make decisions faster and more accurate. But, despite what some purport, the fact remains that "training and learning" are still only as good as the data it uses -- and as such can be prone to all of the same cognitive biases that plague human decision-makers. These models account for most of the digital decisions AI systems make today, using information from behavioral, physiological, or any other source their creators deem relevant. Artificial intelligence has grown exponentially, but how can organizations ensure that AI doesn't make mistakes that are more costly than the technology itself? Cognitive bias is a real problem for AI developers trying to create systems that can make fair and unbiased decisions.

There is a lot of confusion out there about what cognitive bias really is and how it relates to artificial intelligence. One of the most important things to keep in mind is that human and machine cognitive biases are quite different things. Humans and machines can both have biases, but those biases are not the same. While Applied-AI is still in its early days, it is already changing tons of business processes around us and will continue to do so. At a time when the value of data is never higher, many companies are investing in "artificial intelligence," in one or the other form, to help them transform business processes and make decisions faster and more accurate.