Explainability in Machine Learning

March 28-29th, 2023  |  Alte Aula, Tübingen, Germany

Deep learning has enabled major advances in machine learning. However, the deployment of deep learning frameworks in settings that are safety-critical or that impact society requires their decision-making to be explainable. This is fundamental for building trustworthy and user-oriented machine learning models. The aim of this workshop is to generate awareness around explainability in machine learning which is a topic of growing interest. Furthermore, we aim to encourage interdisciplinary interaction and collaboration between researchers from the University of Tübingen and other international institutions that work on different aspects of explainability, in particular in the context of computer vision.

Our workshop will consider research on approaches to explainability and on applications of explainability. Topics of interest will include (but will not be limited to):

  • Computer vision (and visual explanations)
  • Natural language processing
  • Explanations for medical applications
  • Autonomous driving
  • Fairness
  • Causality

Program and Venue


The workshop will contain both keynote talks from known researchers in the field as well as invited talks and spotlight presentations of recent advancements in the field of explainability.

9:00-9:20Opening remarks9:00-9:50Invited talk: Bernt Schiele
"Interpretability of Deep Learning Medical Image Computing Technologies: Insights, Challenges and Opportunities""Interpretability for Deep Learning in Computer Vision"
9:20-10:10Invited talk: Wojciech Samek9:50-10:15Spotlight: Moritz Böhle
"Concept-Level Explainable AI"
10:10-10:40Coffee break10:15-10:40Coffee break
10:40-11:05Spotlight: Diego Marcos10:40-11:05Spotlight: Leon Sixt
11:05-11:30Spotlight: Maximilian Augustin11:05-11:30Spotlight: Seong Joon Oh
11:30-12:20Invited talk:
Ulrike von Luxburg and Sebastian Bordt
11:30-12:20Invited talk: Mara Graziani
"Explanation and Regulation""Concept discovery and Dataset exploration with Singular Value Decomposition"
12:20-13:20Lunch break12:20-13:20Lunch break
13:20-13:45Spotlight: Stephan Alaniz13:20-13:45Spotlight: Sara Blanco
13:45-14:10Spotlight: Julius von Kügelgen
13:45-14:35Invited talk: Ruth Fong14:10-15:00Invited talk: Mauricio Reyes
"Directions in Interpretability""Interpretability of Deep Learning Medical Image Computing Technologies: Insights, Challenges and Opportunities"
14:35-15:05Coffee break15:00-15:15Closing remarks
15:05-15:30Spotlight: Katrin Renz
15:30-15:55Spotlight: Roland Zimmermann
16:00-16:50Invited talk (hybrid):
Trevor Darrell and Lisa Dunlap
"Moving from Explainable models to Advisable models"
17:00-18:00Panel discussion


The workshop will take place at the Alte Aula in Tübingen, 28-29th March 2023. Alte Aula is the old auditorium, a historic building whose construction dates back to the 16th century.

Participants will also have the chance to explore the old university town of Tübingen. The city combines the ancient medieval atmosphere with the vibrant life of a cosmopolitan student town. You can get lost in the narrow alleys and timber-framed houses that lead to the 500-years old castle, take a boat trip in the famous "Stocherkahn" or enjoy the tasteful Swabian cuisine. For more information on the city, visit www.tuebingen.de.


There is no registration fee, however the venue has limited capacity. If you would like to attend, please register by February 28th using this form. Sorry, registrations are closed now!

Childcare funds

The workshop can offer limited childcare grants. Please indicate if you require childcare support when registering your interest to attend.


Do you have any questions? Please send an email to eml-workshop@inf.uni-tuebingen.de.


Ruth Ruth Fong

Princeton University

Mara Mara Graziani

IBM & HES-SO Valais

Mauricio Mauricio Reyes

Univ. of Bern

Wojciech Wojciech Samek

Fraunhofer HHI & TU Berlin

Bernt Bernt Schiele

MPI for Informatics & Saarland


Ulrike Ulrike von Luxburg

Univ. of Tübingen


Zeynep Zeynep Akata

Univ. of Tübingen

Stephan Stephan Alaniz

Univ. of Tübingen

Christian Christian Baumgartner

Univ. of Tübingen

Almut Almut Sophia Koepke

Univ. of Tübingen

Massimiliano Massimiliano Mancini

Univ. of Tübingen

Seong Seong Joon Oh

Univ. of Tübingen

Workshop funded by the
"Cluster of Excellence -
Machine Learning: New Perspectives for Science"