Connectionists: XKDD2022 - Extended Deadline to 4 July

Wed Jun 15 09:25:38 EDT 2022

XKDD 2022 - Call for Papers
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4th International Workshop on eXplainable Knowledge Discovery in Data Mining
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Due to the many requests received we decided to extend the submission to
July 4, 2022.

IMPORTANT DATES
Paper Submission deadline: July 4, 2022
Accept/Reject Notification: July 20, 2022
Camera-ready deadline: July 31, 2022
Workshop: September 19, 2022

CONTEXT & OBJECTIVES
In the past decade, machine learning based decision systems have been
widely used in a wide range of application domains,  like credit score,
insurance risk, and health monitoring, in which accuracy is of the utmost
importance.
Although the support of these systems has an immense potential to improve
the decision in different fields, their use may present ethical and legal
risks, such as codifying biases, jeopardizing transparency and privacy, and
reducing accountability.
Unfortunately, these risks arise in different applications. They are made
even more serious and subtly by the opacity of recent decision support
systems, which are often complex and their internal logic is usually
inaccessible to humans.

Nowadays, most Artificial Intelligence (AI) systems are based on Machine
Learning algorithms.
The relevance and need for ethics in AI are supported and highlighted by
various initiatives arising from the researches to provide recommendations
and guidelines in the direction of making AI-based decision systems
explainable and compliant with legal and ethical issues.
These include the EU's GDPR regulation which introduces, to some extent, a
right for all individuals to obtain ``meaningful explanations of the logic
involved'' when automated decision making takes place, the ``ACM Statement
on Algorithmic Transparency and Accountability'', the Informatics Europe's
``European Recommendations on Machine-Learned Automated Decision Making''
and ``The ethics guidelines for trustworthy  AI'' provided by the  EU
High-Level Expert Group on AI.

The challenge to design and develop trustworthy AI-based decision systems
is still open and requires a joint effort across technical, legal,
sociological and ethical domains.

The purpose of XKDD, eXplainable Knowledge Discovery in Data Mining, is to
encourage principled research that will lead to the advancement of
explainable, transparent, ethical and fair data mining and machine learning.
The workshop will seek top-quality submissions related to ethical, fair,
explainable and transparent data mining and machine learning approaches.
Also, this year the workshop will seek submissions addressing uncovered
important issues in specific fields related to eXplainable AI (XAI), such
as privacy and fairness, application in real case studies, benchmarking,
explanation of decision systems based on time series and graphs which are
becoming more and more important in nowadays applications.
Papers should present research results in any of the topics of interest for
the workshop, as well as tools and promising preliminary ideas.
XKDD asks for contributions from researchers, academia and industries,
working on topics addressing these challenges primarily from a technical
point of view but also from a legal, ethical or sociological perspective.

Topics of interest include, but are not limited to:

TOPICS
 -  Explainable Artificial Intelligence (XAI)
 -  Interpretable Machine Learning
 -  Transparent Data Mining
 -  XAI for Fairness Checking approaches
 -  XAI for Privacy-Preserving Systems
 -  XAI for Federated Learning
 -  XAI for Time Series based Approaches
 -  XAI for Graph-based Approaches
 -  XAI for Visualization
 -  XAI in Human-Machine Interaction
 -  XAI Benchmarking
 -  XAI Case studies
 -  Counterfactual Explanations
 -  Ethics Discovery for Explainable AI
 -  Privacy-Preserving Explanations
 -  Transparent Classification Approaches
 -  Explanation, Accountability and Liability from an Ethical and Legal
Perspective
 -  Iterative Dialogue Explanations
 -  Explanatory Model Analysis
 -  Human-Model Interfaces
 -  Human-Centered Artificial Intelligence
 -  Human-in-the-Loop Interactions
 -  XAI Case Studies and Applications

SUBMISSION & PUBLICATION
All contributions will be reviewed by at least three members of the Program
Committee. As regards size, contributions can be up to 16 pages in LNCS
format, i.e., the ECML PKDD 2022 submission format. All papers should be
written in English. The following kinds of submissions will be considered:
research papers, tool papers, case study papers and position papers.
Detailed information on the submission procedure is available at the
workshop web page:

https://kdd.isti.cnr.it/xkdd2022/

Accepted papers will be published after the workshop by Springer in a
volume of Lecture Notes in Computer Science (LNCS). The condition for
inclusion in the post-proceedings is that at least one of the co-authors
registered to ECML-PKDD and presented the paper at the workshop.
Pre-proceedings will be available online before the workshop. We also allow
accepted papers to be presented without publication in the conference
proceedings if the authors choose to do so. Some of the full paper
submissions may be accepted as short papers after review by the Program
Committee. A special issue of a relevant international journal with
extended versions of selected papers is under consideration.

The submission link is: https://easychair.org/conferences/?conf=xkdd2022

IMPORTANT DATES
Paper Submission deadline: July 4, 2022
Accept/Reject Notification: July 20, 2022
Camera-ready deadline: July 31, 2022
Workshop: September 19, 2022

PROGRAM CO-CHAIRS
* Przemyslaw Biecek, Warsaw University of Technology, Poland
* Riccardo Guidotti, University of Pisa, Italy
* Francesca Naretto, Scuola Normale Superiore, Pisa, Italy
* Andreas Theissler, Aalen University of Applied Sciences, Aalen, Germany

PROGRAM COMMITTEE
* Leila Amgoud, CNRS, France
* Francesco Bodria, Scuola Normale Superiore, Italy
* Umang Bhatt, University of Cambridge, UK
* Miguel Couceiro, INRIA, France
* Menna El-Assady, AI Center of ETH, Switzerland
* Josep Domingo-Ferrer, Universitat Rovira i Virgili, Spain
* Françoise Fessant, Orange Labs, France
* Andreas Holzinger, Medical University of Graz, Austria
* Thibault Laugel, AXA, France
* Paulo Lisboa, Liverpool John Moores University, UK
* Marcin Luckner, Warsaw University of Technology, Poland
* John Mollas, Aristotle University of Thessaloniki, Greece
* Ramaravind Kommiya Mothilal, Everwell Health Solutions, India
* Amedeo Napoli, CNRS, France
* Roberto Prevete, University of Napoli, Italy
* Antonio Rago, Imperial College London, UK
* Jan Ramon, INFRIA, France
* Xavier Renard, AXA, France
* Mahtab Sarvmaili, Dalhousie University, Canada
* Christin Seifert, University of Duisburg-Essen, Germany
* Udo Schlegel, Konstanz University, Germany
* Mattia Setzu, University of Pisa, Italy
* Dominik Slezak, University of Warsaw, Poland
* Fabrizio Silvestri, Università di Roma, Italy
* Francesco Spinnato, Scuola Normale Superiore, Italy
* Vicenc Torra, Umea University, Sweden
* Cagatay Turkay, University of Warwick, UK
* Marco Virgolin, Chalmers University of Technology, Netherlands
* Martin Jullum,  Norwegian Computing Center, Norway
* Albrecht Zimmermann, Université de Caen, France
* Guangyi Zhang, KTH Royal Institute of Technology, Sweden

INVITED SPEAKERS
* Prof. Wojciech Samek, TU Berlin
* Prof. Anna Monreale, University of Pisa

PARTICIPATION
ECML-PKDD 2022 plans a hybrid organization for workshops.
Therefore a person can attend an online event as long as she/he registers
for the conference by using the video conference registration fee:
https://2022.ecmlpkdd.org/index.php/registration/.
Please note the video conference registration fee also allows you to follow
the main conference. However, for an in-person event, interactions and
discussions are much easier face-to-face.
Thus, we believe that it is important that speakers attend in-person
workshops to get fruitful events, and we highly encourage authors of
submitted papers to plan to participate on-site at the event.

CONTACT
All inquiries should be sent to xkdd2022 at easychair.org

-- 
Francesca Naretto
Ph.D. student in Data Science
francesca.naretto at sns.it
SNS, Pisa | CNR, Pisa
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