[Ieee_vis] CFP - Vis & ML for XAI Special Session of ICPRAI 2022

[Romain Bourqui]

  Vis&ML for XAI - Special Session of ICPRAI 2022


  Bridging the Gap between ML and Visualization communities for
  eXplainable Artificial Intelligence


<https://bgmv-xai.labri.fr/> 	
ICPRAI 2022 - 3rd International Conference on Pattern Recognition and 
Artificial Intelligence

<https://icprai2022.sciencesconf.org/>
June 1-^3, 2022
Doctoral consortium: May 31, 2022
Paris (France) <https://icprai2022.sciencesconf.org/>


  Call for paper


    About

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 
<https://icprai2022.sciencesconf.org/>, endorsed by IAPR, is an 
opportunity to fill the gap between Machine Learning and Information 
Visualization communities and to promote new joint research paths.


    Topics

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

  * Trust, Uncertainty, Fairness, Accountability and Transparency
  * Explainable/Interpretable Machine Learning
  * Information visualization for models or their predictions
  * Interactive applications for XAI
  * XAI Evaluation and Benchmarks
  * Human-AI interface and interaction design
  * Sample-centric and Dataset-centric explanations
  * Attention mechanisms for XAI
  * Pruning with XAI

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

  * David Auber, France, Univ. Bordeaux / LaBRI
  * Thomas Baltzer Moeslund, Denmark, Aalborg University / Visual
    Analysis and Perception Laboratory
  * Alexandre Benoit, France, University Savoie Mont Blanc / LISTIC
  * Jenny Benois-Pineau, France, Univ. Bordeaux / LaBRI
  * Romain Bourqui, France, Univ. Bordeaux / LaBRI
  * André CPLF de Carvalho, Brazil, University of Sao Paulo / ICMC
  * Romain Giot, France, Univ. Bordeaux / LaBRI
  * Christophe Hurter, France, Ecole Nationale de l’Aviation Civile
  * Mark Keane, Ireland, UCD Dublin / Insight SFI Centre for Data Analytics
  * Stefanos Kollias, Greece, National Technical University of Athens /
    Image, Video and Multimedia Systems Lab
  * Sebastian Lapuschkin, Germany, Fraunhofer Institute for
    Telecommunications
  * Grégoire Montavon, Germany, Universität Berlin
  * Harold Mouchere, France, Université de Nantes / LS2N
  * Luis Gustavo Nonato, Brazil, University of São Paulo / Instituto de
    Ciencias Matematicas e de Computacao
  * Dragutin Petkovic, USA, San Francisco State University
  * Wojciech Samek, Germany, Fraunhofer Heinrich Hertz Institute
  * Nicolas Thome, France, CNAM/Cedric
  * Alex Telea, Nederland, Utrecht University / Department of
    Information and Computing Sciences
  * Romain Vuillemot, France, ENS Lyon / LIRIS


  Paper submission

A paper can be submitted via the EasyChair online submission system at 
the following address: 
https://easychair.org/my/conference?conf=icprai2022 
<https://easychair.org/my/conference?conf=icprai2022>. 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 
<https://www.springer.com/fr/computer-science/lncs/conference-proceedings-guidelines> 
and templates 
<https://resource-cms.springernature.com/springer-cms/rest/v1/content/19238648/data/v1> 
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 <http://www.springer.com/> in the Lecture Notes in 
Computer Science <http://www.springer.com/gp/computer-science/lncs>. 
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

  * |15/12/2021|: Submission deadline
  * |08/03/2022|: Author notification
  * |22/03/2022|: Camera ready deadline
  * |01/06/2022--03/06/2022|: ICPRAI 2022 - 3rd International Conference
    on Pattern Recognition and Artificial Intelligence


  Special Session organizes

  * Romain Bourqui, France, Univ. Bordeaux / LaBRI
    <mailto:romain.bourqui+icprai at u-bordeaux.fr>
  * Romain Giot, France, Univ. Bordeaux / LaBRI
    <mailto:romain.giot+icprai at u-bordeaux.fr>
  * Wojciech Samek, Germany, Fraunhofer Heinrich Hertz Institute
    <mailto:wojciech.samek+icprai at hhi.fraunhofer.de>

Univ. Bordeaux <http://u-bordeaux.fr> LaBRI <http://labri.fr> CNRS 
<http://cnrs.fr> Fraunhofer HHI <https://www.hhi.fraunhofer.de/>
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