Publication
Interaction with Explanations in the XAINES Project
Mareike Hartmann; Ivana Kruijff-Korbayová; Daniel Sonntag
Trustworthy AI in the Wild Workshop 2021, 9/2021.
Abstract
AI systems are increasingly pervasive and their large-scale adoption makes it necessary to explain their behaviour, for example to their users who are impacted by their decisions, or to their developers who need to ensure their functionality. This requires, on the one hand, to obtain an accurate representation of the chain of events that caused the system to behave in a certain way (e.g., to make a specific decision). On the other hand, this causal chain needs to be communicated to the users depending on their needs and expectations. In this phase of explanation delivery, allowing interaction between user and model has the potential to improve both model quality and user experience. In this abstract, we present our planned and on-going work on the interaction with explanations as part of the XAINES project. The project investigates the explanation of AI systems through narratives targeted to the needs of a specific audience, and our work focuses on the question of how and in which way human-model interaction can enable successful explanation.