Visual Knowledge Discovery with Artificial Intelligence: Challenges and Future Directions
It tackles the problem of model interpretability for AI practitioners and researchers, but appears to be an incremental survey/discussion paper.
This paper addresses the challenge of explaining AI/ML models to humans by integrating visualization and visual analytics, presenting progress in areas like Full 2D ML and NLP with visual aids.
This volume is devoted to the emerging field of Integrated Visual Knowledge Discovery that combines advances in Artificial Intelligence/Machine Learning (AI/ML) and Visualization/Visual Analytics. Chapters included are extended versions of the selected AI and Visual Analytics papers and related symposia at the recent International Information Visualization Conferences (IV2019 and IV2020). AI/ML face a long-standing challenge of explaining models to humans. Models explanation is fundamentally human activity, not only an algorithmic one. In this chapter we aim to present challenges and future directions within the field of Visual Analytics, Visual Knowledge Discovery and AI/ML, and to discuss the role of visualization in visual AI/ML. In addition, we describe progress in emerging Full 2D ML, natural language processing, and AI/ML in multidimensional data aided by visual means.