This is where I collect and write things, mostly on academe and artificial intelligence. Established in 2010, by Vaishak Belle.
There are many dimensions to explanations in AI. In the case of machine learning models, we might be interested in understanding how blackbox systems work, which might range anything from understanding the influence of data points to looking critically at which features influence the classification. When we want to go beyond prediction, it becomes much more important to understand stakeholder engagement and handle input from the user. Follow up questions need to be entertained. Some kind of counterfactual argument, contrastive argument or explanations involving repeated back and forth might be needed. Ideally the system offers the simplest explanation and expands this when clarification is requested.