How to set realistic expectations for AI

On July 27, 2018, news broke that collaborative efforts in cancer treatment between the Sloan Kettering Research Institute and IBM were producing instances of treatment prescriptions that were unsuited for the patients the treatments were prescribed for. In one case, a 65-year-old man was prescribed a drug that could lead to "severe or fatal hemorrhage" even though he was already suffering from severe bleeding When investigators dug deeper, they found that IBM engineers and Sloan Kettering medical doctors had fed hypothetical patient data to IBM's Watson, which was processing the treatment analytics. Initial thinking was that the use of hypotheticals (instead of real patient data) potentially skewed the AI and resulted in multiple examples of unsafe or incorrect treatment recommendations. At first blush, it is easy to slam the AI--but should we? The most important lesson to be learned from Watson or any other AI technology that is being trialed in business right now is that AI isn't perfect.