Digital Collaborator: Augmenting Task Abstraction in Visualization Design with Artificial Intelligence
This addresses a specific challenge for visualization practitioners, but it is incremental as it builds on existing theory without introducing a new method or showing results.
The paper tackles the problem of errors in the manual task abstraction phase of visualization design, which arise from designer biases and lack of domain knowledge, by proposing a conceptual AI system called Digital Collaborator to help practitioners validate and reason about task abstraction outputs.
In the task abstraction phase of the visualization design process, including in "design studies", a practitioner maps the observed domain goals to generalizable abstract tasks using visualization theory in order to better understand and address the users needs. We argue that this manual task abstraction process is prone to errors due to designer biases and a lack of domain background and knowledge. Under these circumstances, a collaborator can help validate and provide sanity checks to visualization practitioners during this important task abstraction stage. However, having a human collaborator is not always feasible and may be subject to the same biases and pitfalls. In this paper, we first describe the challenges associated with task abstraction. We then propose a conceptual Digital Collaborator: an artificial intelligence system that aims to help visualization practitioners by augmenting their ability to validate and reason about the output of task abstraction. We also discuss several practical design challenges of designing and implementing such systems