03. – 08. November 2019, Dagstuhl-Seminar 19452

Machine Learning Meets Visualization to Make Artificial Intelligence Interpretable


Enrico Bertini (NYU – Brooklyn, US)
Peer-Timo Bremer (LLNL – Livermore, US)
Daniela Oelke (Siemens AG – München, DE)
Jayaraman Thiagarajan (LLNL – Livermore, US)

Auskunft zu diesem Dagstuhl-Seminar erteilen

Annette Beyer zu administrativen Fragen

Shida Kunz zu wissenschaftlichen Fragen


Gemeinsame Dokumente
Programm des Dagstuhl-Seminars [pdf]


The recent advances in machine learning have led to unprecedented successes in areas such as computer vision, natural language processing, or medicine. In the future, these technologies promise to revolutionize science and technology by producing everything from self-driving cars to new in- sights into large-scale scientific experiments. However, one of the fundamental challenges in making this vision a reality is that, while it is possible today to create working solutions for complex tasks, the resulting systems are typically black boxes. This means one does not know how or why a certain decision has been reached, whether the input data was sufficient and unbiased, or how robust and reliable the system might be for new data. These challenges are often summarized as a lack of “interpretability” of the models, which leads to a lack of trust in the solutions, which in turn limits or even prevents applications from fully exploiting the potential benefits of machine learning.

In response, the machine learning community as well as virtually all application areas have seen a rapid expansion of research efforts in interpretability and related topics. In the process, visualization, or more generally interactive systems, have become a key component of these efforts since they provide one avenue to exploit expert intuition and hypothesis driven exploration. However, due to the unprecedented speed in which the field is currently progressing, it is difficult for the various communities to maintain a cohesive picture of the state of the art and the open challenges, especially given the extreme diversity of research areas affected. This has led to a certain fragmentation in which different application areas, terminology, and disparate communities can obscure the common goals and research objectives.

This Dagstuhl Seminar aims to alleviate this problem by bringing together various stakeholders to jointly discuss needs, characterize open research challenges, and propose a joint research agenda. In particular, there appear to be three groups of stakeholders: application experts with unmet needs and practical problems; machine learning researchers who are the main source of theoretical advances; and visualization and HCI experts that can devise intuitive representations and exploration frameworks for practical solutions. The goal of this seminar is to bring all three communities together in order to: 1) Assemble an overview of existing approaches and research directions; 2) Understand shared research challenges and current gaps; and 3) Formulate a joint research agenda to guide research in this critical area.

Furthermore, we expect the personal connections which are the hallmark of all Dagstuhl Seminars to provide a force multiplier in future research.

Motivation text license
  Creative Commons BY 3.0 DE
  Enrico Bertini, Peer-Timo Bremer, Daniela Oelke, and Jayaraman Thiagarajan


  • Artificial Intelligence / Robotics
  • Society / Human-computer Interaction


  • Visualization
  • Machine Learning
  • Interpretability


In der Reihe Dagstuhl Reports werden alle Dagstuhl-Seminare und Dagstuhl-Perspektiven-Workshops dokumentiert. Die Organisatoren stellen zusammen mit dem Collector des Seminars einen Bericht zusammen, der die Beiträge der Autoren zusammenfasst und um eine Zusammenfassung ergänzt.


Download Übersichtsflyer (PDF).


Es besteht weiterhin die Möglichkeit, eine umfassende Kollektion begutachteter Arbeiten in der Reihe Dagstuhl Follow-Ups zu publizieren.

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