Why people don't trust artificial intelligence: It's an 'explainability' problem Genetic Literacy Project

Despite its promise, the growing field of Artificial Intelligence (AI) is experiencing a variety of growing pains. In addition to the problem of bias I discussed in a previous article, there is also the'black box' problem: if people don't know how AI comes up with its decisions, they won't trust it. In fact, this lack of trust was at the heart of many failures of one of the best-known AI efforts: IBM Watson – in particular, Watson for Oncology. If oncologists had understood how Watson had come up with its [diagnoses] – what the industry refers to as'explainability' – their trust level may have been higher. "The more complex a system is, the less explainable it will be," says John Zerilli, postdoctoral fellow at University of Otago and researcher into explainable AI. "If you want your system to be explainable, you're going to have to make do with a simpler system that isn't as powerful or accurate."