Connectionists: XKDD2022 Call for Papers

Tue Jun 28 03:45:32 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: June 20, 2022
Accept/Reject Notification: July 13, 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.

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