Vis&ML for XAI - Bridging the Gap between ML and Visualization communities for eXplainable Artificial Intelligence


June 1- 3, 2022
Doctoral consortium: May 31, 2022

Call for paper


The rise of machine learning approaches, and in particular deep learning, has led to a significant increase in the performance of AI systems. However, it has also raised the question of the reliability and explicability of their predictions for decision-making (i.e., the black-box issue of the deep models). Such shortcomings also raise many ethical and political concerns that prevent wider adoption of this potentially highly beneficial technology, especially in critical areas, such as healthcare, self-driving cars or security. It is therefore critical to understand how their predictions correlate with information perception and expert decision-making. The objective of eXplainable AI (XAI) is to open this black-box by proposing methods to understand and explain how these systems produce their decisions.

Research work in XAI is currently carried out in parallel by the Machine Learning and the Information Visualization communities using methodologies and competencies from their own field. This special session hosted by the ICPRAI conference, endorsed by IAPR, is an opportunity to fill the gap between Machine Learning and Information Visualization communities and to promote new joint research paths.


Here are the main, but not limited to, topics of interest:

We expect papers written by researchers from both communities, with a preference for works that imply a joint research (e.g., visualization experts with machine learning experts). Paper selection will be achieved by a program committee of experts in Machine Learning and experts in Information Visualization; additionally, each paper will be reviewed by at least one expert of the two communities.

Program Comittee

Paper submission

A paper can be submitted via the EasyChair online submission system at the following address: You must select the item ICPRAI 2022 - SS - Vis&ML for XAI: Bridging the gap between machine learning and visualization communities for eXplainable Artificial Intelligence.

Paper guidelines

Articles should be prepared according to the LNCS author guidelines and templates and they should be at most twelve pages long. All papers must be submitted in electronic format as PDF files before the submission deadline.

All papers are subject to a single-blind review process.

Accepted papers will be presented at the conference and will be published by Springer in the Lecture Notes in Computer Science. Before publication, the authors will be requested to fill and sign Springer’s form for the consent to publish and the copyright transfer.

Keep in mind that papers that do not meet the guidelines will be returned to the authors. The articles will be allowed to go through the reviewing process only if they satisfy the specified requirements.

Important dates

Special Session organizes