ETMLP 2020

International Workshop on Explainability for Trustworthy ML Pipelines

Co-located with EDBT 2020 (30 March 2020, Copenhagen, Denmark)

Due to the unprecedented spread of the Coronavirus disease, the co-chairs of the ETMLP workshop have decided to cancel the physical event, and handle it in a virtual format. The instructions for virtual presentations have been already sent to presenters and panelists. Moreover, the keynote is unfortunately fully cancelled.

The workshop proceedings are now published by CEUR at http://ceur-ws.org/Vol-2578. Here is also the DBLP page for the proceeding: https://dblp.dagstuhl.de/db/conf/edbt/edbt2020w.html.

ETMLP 2020

Virtual Workshop

The video presentations for accepted papers and the Q&A with the panelists can be found below.

Monday March 30, 2020

Keynote by Yanlei Diao (cancelled)

Session 1 (Playlist of all video presentations)

Panel on Explainability for Trustworthy ML Pipelines

  • Panelists: Fosca Giannotti, Helena Kotthaus, Katharina Morik, Nico Piatkowski, Philipp Schlunder.
  • Link to Q&A with the panelists

Session 2 (Playlist of all video presentations)

  • Towards Unsupervised Data Quality Validation on Dynamic Data
    by Sergey Redyuk, Volker Markl and Sebastian Schelter (Technische Universität Berlin, Germany)
    Link to presentation video
    Contact for questions
  • Putting the Human Back in the AutoML Loop
    by Iordanis Xanthopoulos (University of Crete), Ioannis Tsamardinos (University of Crete), Vassilis Christophides (University of Crete), Eric Simon (SAP France) and Alejandro Salinger (SAP SE)
    Link to presentation video
    Contact for questions

Home

Machine learning (ML) is a driving force for many successful applications in Artificial Intelligence. ML pipelines ensure guarantees on the entirety of the system (i.e., horizontal certification) as well as on each single component (i.e., vertical certification). The horizontal certification covers the full pipeline from data acquisition to data visualization. Moreover, it spans over user-centered, technical, financial, and regulatory aspects of the system. The vertical certification exploits the theory of ML to guarantee error bounds, sampling complexity, energy consumption, execution time, time-to-think, and memory and communication demands. The understandability of an ML pipeline in its entirety requires the collaboration of researchers from the database and the ML communities.

ETMLP workshop will examine the aforementioned opportunities and their associated challenges. The main objective of this workshop is to create a forum where researchers from machine learning, data management, and practitioners engage with ideas around explainability and certified trustworthiness of ML pipelines, at the pipeline level, as well as the component level.

The ultimate goal of the workshop is to discuss recommendations for further work in science and industry and society regarding explainable ML pipelines.

Important dates

Workshop date: 30 March 2020

Submissions: 20 December 2019 12 January 2020, 11:59PM CET

Notification of outcome: 31 January 2020, 11:59PM CET (before EDBT 2020 early registration deadline)

Camera ready due: 4 February 2020, 11:59PM CET

Submission

The goal of the workshop is to reach a more comprehensive view of the methods and measures for explainability and trustworthiness of machine learning pipelines by bringing together researchers from machine learning, data mining, and databases.
Topics of interest include but are not limited to the followings: challenges of explainable AI, explaining black-box ML models, interpretable machine learning, error and uncertainty bounds, traceability and provenance of ML Pipelines, visual analytics for enabling explanations, robust methods, algorithmic bias, fairness-aware machine learning.

We invite authors to submit either of the following:

  • regular papers: original, unpublished research papers that are not being considered for publication in any other venue;
  • abstract papers: for visionary and work-in-progress ideas.

Regular papers should be maximally 6 pages in length. Abstracts should be maximally one page long. Both page limits exclude references. Papers must follow the latest ACM Proceedings format 2020 (sigconf double-column). The ETMLP 2020 workshop is single-blind.

Submissions will be handled through EasyChair: https://easychair.org/conferences/?conf=etmlp2020

Committee

Workshop Chairs

Behrooz Omidvar-Tehrani (NAVER LABS Europe, co-chair)
Katharina Morik (TU Dortmund, co-chair)
Jean-Michel Renders (NAVER LABS Europe, co-chair)

Program Committee

Sihem Amer-Yahia (CNRS, France)
Vassilis Christophides (University of Crete, Greece and Institute for Advanced Studies, Cergy France)
Joao Comba (UFRGS,Brazil)
Yanlei Diao (Ecole Polytechnique, France)
Fosca Giannotti (Information Science and Technology Institute A. Faedo, Italy)
Dimitrios Gunopulos (National and Kapodistrian University of Athens, Greece)
Thomas Liebig (Materna Information and Communications SE, Germany)
Adrian Mos (NAVER LABS Europe, France)
Dino Pedreschi (University of Pisa, Italy)
Nico Piatkowski (TU Dortmund, Germany)
Stefan Rüping (Fraunhofer Institute for Intelligent Analysis and Information Systems, Germany)
Eric Simon (SAP, France)
Divesh Srivastava (AT&T Labs, USA)
Thibaut Thonet (NAVER LABS Europe, France),
Ioannis Tsamardinos (University of Crete, Greece)

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