Explanatory Dialogs: Towards Actionable, Interactive Explanations

Adoption of AI systems in high-stakes domains (e.g., transportation, law, and healthcare) demands that human users trust these systems. A desiderata for establishing trust is that the users understand the system's decision process. However, a high-performing system may use a complex decision process, which may not be interpretable by itself. We argue that existing solutions for generating interpretable explanations have limitations and as a solution, propose developing new explanation systems that enable interactive and actionable dialogs between the user and the system.