[Infovis] Call for submissions - Research Topic: Visualizing the Unseen Characteristics of Machine Learning Models - Frontiers in Computer Science

[Andreas Kerren]

*** Apologies for cross-posting ***

Dear colleagues,

We would like to bring to your attention and invite your submission to the following Research Topic of the journal Frontiers in Computer Science (section: Computer Graphics and Visualization), which we are currently organizing. A research topic in a Frontiers journal is similar to the more traditional "special issues", where a collection of high-quality papers with similar themes and research directions is gathered and published together. Please check the website linked below for more details. All your submissions are welcome!

# Link
https://www.frontiersin.org/research-topics/59198/visualizing-the-unseen-characteristics-of-machine-learning-models

# Title
Visualizing the Unseen Characteristics of Machine Learning Models

# Short Summary
This Research Topic aims to highlight research and perspectives from both academia and industry on how visualization techniques can improve AI applications by representing data related to the unseen characteristics of machine learning (ML) models. Contributions are welcome on all aspects of the use of visualization and interactive visual analytics for building, improving, and deploying ML models, as well as for interpreting and explaining different types of ML models in order to improve fairness, transparency, accountability, trustworthiness, and other characteristics relevant to all steps of the ML pipeline.

We accept surveys, novel results, and position papers. Topics of interest include, but are not limited to:
 - Conceptual explorations on the use of visualization for improving access to and trust in ML services, such as frameworks, taxonomies, ontologies, etc.
 - Novel visual abstractions and interactive techniques for interpreting and explaining different types of ML models.
 - ML model engineering, improvement, and deployment supported by interactive visualization.
 - Visual and interactive ML-driven tools and applications for specific knowledge domains and problem scenarios.
 - Explicit visual and/or interactive mechanisms for providing guidance and provenance regarding concepts such as trustworthiness, transparency, accountability, etc., for all steps in the ML pipeline.

# Important deadlines
 * Summary submission: February 29th, 2024  (optional, but highly encouraged)
 * Full manuscript: May 31st, 2024

Inquiries can be made to any of the topic editors.

# Topic Editors: 
Carla M D S Freitas (Federal University of Rio Grande do Sul, Brazil), carla at inf.ufrgs.br
Andreas Kerren (Linköping University, Sweden), andreas.kerren at liu.se
Rafael M. Martins (Linnaeus University, Sweden), rafael.martins at lnu.se