Connectionists: XKDD 2021 - Call for Papers - Extended Deadline

Mon Jun 21 09:19:39 EDT 2021

XKDD 2021 - Call for Papers
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3rd International Workshop on eXplainable Knowledge Discovery in Data Mining
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IMPORTANT DATES
Paper Submission deadline: July 7, 2021
Accept/Reject Notification: July 21, 2021
Camera-ready deadline: Aug 14, 2021
Workshop: September 13, 2021

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

Nowadays most of the Artificial Intelligence (AI) systems are based on
Machine Learning algorithms. The relevance and need of ethics in AI is
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, eXaplaining 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. XKDD is an event organized into two moments: a tutorial to
introduce audience to the topic, and a workshop to discuss recent advances
in the research field. The tutorial will provide a broad overview of the
state of the art on the major applications for explainable and transparent
approaches and their relationship with fairness and privacy. Moreover, it
will present Python/R  libraries that practically shows how explainability
and fairness tasks can be addressed. The workshop will seek top-quality
submissions addressing uncovered important issues related to ethical, fair,
explainable and transparent data mining and machine learning. Papers should
present research results in any of the topics of interest for the workshop
as well as application experiences, 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
 -  Interpretable Machine Learning
 -  Transparent Data Mining
 -  Explainability in Clustering Analysis
 -  Technical Aspects of Algorithms for Explanation
 -  Explaining Black Box Decision Systems
 -  Adversarial Attack-based Models
 -  Counterfactual and Prototype-based Explanations
 -  Causal Discovery for Machine Learning Explanation
 -  Fairness Checking
 -  Fair Machine Learning
 -  Explanation for Privacy Risk
 -  Ethics Discovery for Explainable AI
 -  Privacy-Preserving Explanations
 -  Transparent Classification Approaches
 -  Anonymity and Information Hiding Problems in Comprehensible Models
 -  Case Study Analysis
 -  Experiments on Simulated and Real Decision Systems
 -  Monitoring and Understanding System Behavior
 -  Privacy Risk Assessment
 -  Privacy by Design Approaches for Human Data
 -  Statistical Aspects, Bias Detection and Causal Inference
 -  Explanation, Accountability and Liability from an Ethical and Legal
Perspective
 -  Benchmarking and Measuring Explanation
 -  Visualization-based Explanations
 -  Iterative Dialogue Explanations
 -  Explanatory Model Analysis
 -  Human-Model Interfaces
 -  Human-Centered Artificial Intelligence
 -  Human-in-the-Loop Interactions

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 2021 submission format. All papers should be
written in English and be in LNCS format. The following kinds of
submissions will be considered: research papers, tool papers, case study
papers and position papers. Detailed information on the submission
procedure are available at the workshop web page:

https://kdd.isti.cnr.it/xkdd2021/

Accepted papers will be published after the workshop by Springer in a
volume of Lecture Notes in Computer Science (LNCS). Condition for inclusion
in the post-proceedings is that at least one of the co-authors has
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=xkdd2021

KEYNOTE SPEAKER
- Andreas Holzinger, Human-Centered AI Lab, Medical University of Graz,
Austria

PROGRAM COMMITEE
- Avishek Anand, Leibniz University, Germany
- Umang Bhatt, University of Cambridge, UK
- Francesco Bodria, Scuola Normale Superiore, Italy
- Giuseppe Casalicchio, Ludwig-Maximilians-University of Munich, Germany
- Chaofan Chen, University of Maine, US
- Miguel Couceiro, INFRIA, France
- Josep Domingo-Ferrer, Universitat Rovira i Virgili, Spain
- Thibault Laugel, AXA, France
- Paulo Lisboa, Liverpool John Moores University, UK
- Michael Loizos, Open University of Cyprus, Cyprus
- Marcin Luckner, Warsaw University of Technology, Poland
- Stan Matwin, Dalhousie University, Canada
- Ramaravind Kommiya Mothilal, Everwell Health Solutions, India
- Francesca Naretto, Scuola Normale Superiore, Italy
- Roberto Prevete, University of Napoli Federico II, Italy
- Antonio Rago, Imperial College London, UK
- Jan Ramon, INFRIA, France
- Xavier Renard, AXA, France
- Mahtab Sarvmaili, Dalhousie University, Canada
- Dominik Slezak, University of Warsaw, Poland
- Myra Spiliopoulou, University Magdeburg, Germany
- Kacper Sokol, University of Bristol, UK
- Vicenc Torra, Umea University, Sweden
- Grigorios Tsoumakas, Aristotle University of Thessaloniki, Greece
- Cagatay Turkay, University of Warwick, UK
- Marco Virgolin, Chalmers University of Technology, Sweden
- Wendy Hui Wang, Stevens Institute, USA
- Guangyi Zhang, KTH Royal Institute of Technology, Sweden

PROGRAM CO-CHAIRS
* Przemyslaw Biecek, Warsaw University of Technology, Poland
* Riccardo Guidotti, University of Pisa, Italy
* Anna Monreale, University of Pisa, Italy
* Salvatore Rinzivillo, ISTI-CNR, Pisa,  Italy

CONTACT
All inquires should be sent to xkdd2021 at easychair.org

-- 
Riccardo Guidotti
Dipartimento di Informatica
Università di Pisa, Largo Bruno Pontecorvo, 3, 56127 Pisa
Mail: riccardo.guidotti at unipi.it
Web: http://kdd.isti.cnr.it/homes/guidotti/
KDD Lab, Room: 286
Phone: +39 050 221 3134
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