Justify

Applications need to support interaction with humans in a way which makes outcomes recognizable and believable. For example, when one builds a predictive model, it is important to have an explanation of how the model is doing what it is doing, like what the features in the model are doing in terms that are familiar to the users of the model. This level of familiarity is important in generating trust and intuition. Similarly, in the same way that automobiles have mechanisms not just for detecting the presence of a malfunction, but also for specifying the nature of the malfunction and suggesting a method for correcting it, so one needs to have a nuts-and-bolts understanding of how an application is working in order to “repair” it when it goes awry. There is a difference between transparency and justification. Transparency tells you what algorithms and parameters were used, while justification tells you why. For intelligence to be meaningful, it must be able to justify and explain its assertions, as well as to be able to diagnose failures. No leader should deploy intelligent and autonomous applications against critical business problems without a thorough understanding of what variables power the model. Enterprises cannot move to a model of intelligent applications without trust and transparency.