We consider the influence of two types of contextual information, background information available to users and users’ goals, on users’ views and preferences regarding textual explanations generated for the outcomes predicted by Decision Trees (DTs). To investigate the influence of background information, we generate contrastive explanations that address potential conflicts between aspects of DT predictions and plausible expectations licensed by background information. We define four types of conflicts, operationalize their identification, and specify explanatory schemas that address them. To investigate the influence of users’ goals, we employ an interactive setting where given a goal and an initial explanation for a predicted outcome, users select follow-up questions, and assess the explanations that answer these questions. Here, we offer algorithms to generate explanations that address six types of follow-up questions. The main result from both user studies is that explanations which have a contrastive aspect about a predicted class are generally preferred by users. In addition, the results from the first study indicate that these explanations are deemed especially valuable when users’ expectations differ from predicted outcomes; and the results from the second study indicate that contrastive explanations which describe how to change a predicted outcome are particularly well regarded in terms of helping users’ achieve this goal, and they are also popular in terms of helping users’ achieve other goals.
- Explainable AI
- Generating textual explanations
- Taking context into account
- Contrastive explanations
- Decision trees