ICPR'2020 WorkShop
Explainable Deep Learning- AI

January 11, 2021

organized in conjunction with ICPR2020 The 25th International Conference on Pattern Recognition, Milan, Italy, January 10-15, 2021”

About The Event

.... The Workshop will be held Online accordingly to the general ICPR Policy

.... Camera-ready paper submission: November 18th 2020 23:59 GMT


The recent focus of AI and Pattern Recognition communities on the supervised learning approaches, and particularly to Deep Learning / AI, resulted in considerable increase of performance of Pattern Recognition and AI systems, but also raised the question of the trustfulness and explainability of their predictions for decision-making. Instead of developing and using Deep Learning as a black box and adapting known Neural Networks architectures to variety of problems, the goal of explainable Deep Learning / AI is to propose methods to “understand” and “explain” how the these systems produce their decisions. AI systems may produce errors, can exhibit overt or subtle bias, may be sensitive to noise in the data, and often lack technical and judicial transparency and explainability. These shortcomings raise many ethical and policy concerns that impede wider adoption of this potentially very beneficial technology. In various Pattern Recognition and AI application domains such as health, ecology, autonomous driving cars, security, culture it is mandatory to understand how the predictions are correlated with the information perception and decision making by the experts and impact society and business.. The goals of this full day workshop are to bring together research community which is working on the question of improving explainability of AI and Pattern Recognition algorithms and systems. The Workshop is a part of ICPR'2020 and supported by research project XAI-LABRI


  • “Sensing” or “salient features” of Neural Networks and AI systems - explanation of which features for a given configuration yield predictions both in spatial (images) and temporal (time-series, video) data;
  • Optimal Visualization of salient features and areas in input data contribution into decision making;
  • Attention mechanisms in Deep Neural Networks and their explanation;
  • For temporal data, the explanation of which features and at what time are the most prominent for the prediction and what are the time intervals when the contribution of each data is important;
  • How the explanation can help on making Deep learning architectures more sparse (pruning) and light-weight;
  • When using multimodal data how the prediction in data streams are correlated and explain each other;
  • Automatic generation of explanations / justifications of algorithms and systems’ decisions;
  • Decisional uncertainly and explicability
  • Evaluation of the explanations generated by Deep Learning and other AI systems.

Program Committee

  • Christophe Garcia (LIRIS, France)
  • Dragutin Petkovic (SFSU,USA)
  • Alexandre Benoît( LISTIC,France)
  • Mark T. Keane (UCD, Ireland)
  • Georges Quenot(LIG, France)
  • Stefanos Kolias (NTUA, Grece)
  • Jenny Benois-Pineau(LABRI, France)
  • Hervé Le Borgne (LIST, France)
  • Noel O’Connor (DCU, Ireland)
  • Nicolas Thome(CNAM, France)


  • Submission deadline : October 10th 2020 October 17th 2020 23:59 GMT
  • Workshop author notification: November 10th 2020
  • Camera-ready submission: November 18th 2020
  • Finalized workshop program: December 1st 2020
  • Workshop event: online January 11, 2021

Paper Submission

The Proceedings of the EDL-AI 2020 workshop will be published in the Springer Lecture Notes in Computer Science (LNCS) series. Papers will be selected by a single blind (reviewers are anonymous) review process. All selected papers will be published and subset of them will be presented at the workshop. Submissions must be formatted in accordance with the Springer's Computer Science Proceedings guidelines . Two types of contribution will be considered:

  • Full paper (12-15 pages)
  • Short papers (6-8 pages)

Submission site: Submission

Tentative Workshop Agenda

  • 9:00-9:15 Welcome and overview of the Workshop (workshop organizers)
  • 9:15-10:00 Plenary talk "Towards AI Ethics and Explainability", Prof. D. Petkovic, San Francisco State University, USA
  • 10:00 – 12:00 invited talks
  • 12:00 – 1:30 lunch and mingle
  • 1:30 - 3:00 – selected talks
  • 3:00 - 3:10 Break
  • 3:10 - 4:10 Poster Session
  • 4:10 – 5:40 Panel discussion (5-6 panelists selected by WS PC; moderator D. Petkovic)

    Panelists will each present a 5 min their position and challenges they see, after which the audience will be engaged in moderated discussion
  • 5:40 - 5:50 Closing remarks (WS organizers)

WS Organizers: Jenny Benois-Pineau (LABRI, France),Georges Quenot(LIG, France)

Publication chairs: Romain Bourqui(LABRI,France), Romain Giot(LABRI, France)

Panel chair: Dragutin Petkovic (SFSU,USA)