Towards Interactive Multimodal Representation of ML Functions for Human Understanding of ML
Abstract
Attitudes about artificial intelligence and machine learning are recent victims of endemic misunderstanding; given our increasing reliance on these technologies, the need for widespread understanding and confidence in their use is paramount. To this end, our work seeks to increase understanding in these typically inaccessible topics through interactive visualizations, thereby garnering curiosity in the hopes of kickstarting a cycle of understanding leading to further pursuit of knowledge. We hope this will cyclically shift global attitudes away from the intimidation of the unknown currently plaguing ML. This work explores best practices for supporting curiosity in new technologies, to inspire attitudinal paradigm-shifts. Over three, distinct visualizations of machine learning data, we created prototypes with carefully selected, highly-transparent datasets, to examine the success factors of engagement required for more informed attitudes on ML less dictated by the fear of the unknown. By employing interactive visualizations, we can captivate the interest of teenagers and individuals from diverse fields, encouraging them to explore the fascinating world of machine learning.
Keywords
Cite
@article{arxiv.2605.00357,
title = {Towards Interactive Multimodal Representation of ML Functions for Human Understanding of ML},
author = {Bokang Wang and Yingxuan Liao and Leah Lee and Jack Wesson and Anlan Yang and Ruizi Wang and Yigang Wen},
journal= {arXiv preprint arXiv:2605.00357},
year = {2026}
}