  Explainable AI (XAI) tools represent a turn to more human-centered and
human-in-the-loop AI approaches that emphasize user needs and perspectives in
machine learning model development workflows. However, while the majority of ML
resources available today are developed for Python computational environments
such as JupyterLab and Jupyter Notebook, the same has not been true of
interactive XAI systems, which are often still implemented as standalone
interfaces. In this paper, we address this mismatch by identifying three design
patterns for embedding front-end XAI interfaces into Jupyter, namely: 1)
One-way communication from Python to JavaScript, 2) Two-way data
synchronization, and 3) Bi-directional callbacks. We also provide an
open-source toolkit, bonXAI, that demonstrates how each design pattern might be
used to build interactive XAI tools for a Pytorch text classification workflow.
Finally, we conclude with a discussion of best practices and open questions.
Our aims for this paper are to discuss how interactive XAI tools might be
developed for computational notebooks, and how they can better integrate into
existing model development workflows to support more collaborative,
human-centered AI.
