Call for Papers
[email protected]: Joint International Workshop on Advances in Interpretable Machine Learning and Artificial Intelligence & eXplainable Knowledge Discovery in Data Mining
*Submission link: https://easychair.org/conferences/?conf=aimlai-xkdd-ecmlpkdd19
*Submission deadline: June 7, 2019
The purpose of AIMLAI-XKDD (Advances in Interpretable Machine Learning and Artificial Intelligence & 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, machine learning, and artificial intelligence. AIMLAI-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 and the major applications for explainable and transparent approaches. Likewise it will highlight the main open challenges.
The workshop will seek top-quality submissions addressing uncovered important issues related to explainable and interpretable data mining and machine learning models. 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. AIMLAI-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. Besides the central topic of interpretable algorithms and explanation methods, we also welcome submissions that answer research questions like "how to measure and evaluate interpretability and explainability?" and "how to integrate humans in the machine learning pipeline for interpretability purposes?".
Papers must be written in English and formatted according to the Springer Lecture Notes in Computer Science (LNCS) guidelines following the style of the main conference (format).
The maximum length of either research or position papers is 12 pages in this format. Overlength papers will be rejected without review (papers with smaller page margins and font sizes than specified in the author instructions and set in the style files will also be treated as overlength).
Authors who submit their work to AIMLAI-XKDD 2019 commit themselves to present their paper at the workshop in case of acceptance. AIMLAI-XKDD 2019 considers the author list submitted with the paper as final. No additions or deletions to this list may be made after paper submission, either during the review period, or in case of acceptance, at the final camera ready stage.
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. A special issue of a relevant international journal with extended versions of selected papers is under consideration.
All papers for AIMLAI-XKDD 2019 must be submitted by using the on-line submission system at https://easychair.org/conferences/?conf=aimlai-xkdd-ecmlpkdd19.
*Tutorial Program Chairs
Riccardo Guidotti, KDD Lab, ISTI-CNR, Italy
Pasquale Minervini, University College London, UK
Anna Monreale, KDD Lab, University of Pisa, Italy
Salvatore Rinzivillo, KDD Lab, ISTI-CNR, Italy
*Workshop Program Chairs
Adrien Bibal, University of Namur, Belgium
Tassadit Bouadi, University of Rennes/IRISA, France
Benoît Frénay, University of Namur, Belgium
Luis Galárraga, Inria/IRISA, France
Stefan Kramer, Universität Mainz, Germany
Ruggero G. Pensa, University of Turin, Italy
All accepted papers will be published as post-proceedings in LNCSI and included in the series name Lecture Notes in Computer Science by Springer.
The conference will be colocated with the conference ECML/PKDD, which will be held in the Hubland campus of the University of Würzburg in Germany on September 20th, 2019.
All questions about submissions should be emailed to [email protected]
Credits and Sources
| AIMLAI-XKDD 2019 : Joint International Workshop on Advances in Interpretable Machine Learning and Artificial Intelligence & eXplainable Knowledge Discovery in Data Mining|