Explainable Machine Learning

Zeynep Akata was appointed as Professor for “Explainable Machine Learning” at the Faculty of Sciences in the winter semester 2019/2020. The professorship has been set up within the framework of our Cluster of Excellence "Machine Learning: New Perspectives for the Sciences".

Research Interests

A learning machine that a user can trust and easily operate needs to be fashioned with the ability of explanation. 

While deep neural networks lead to impressive successes, e.g. they can now reliably identify 1000 object classes, argue about their interactions through natural language, answer questions about their attributes through interactive dialogues, integrated interpretability is still in its early stages. In other words, we do not know why these deep learning based visual classifications systems work when they are accurate and why they do not work when they make mistakes. Enabling such transparency requires the interplay of different modalities such as images and text, whereas current deep networks are designed as a combination of different tools each optimising a different learning objective with extremely weak and uninterpretable communication channels. However, deep neural networks draw their power from their ability to process large amounts of data in an end-to-end manner through a feedback loop with forward and backward processing. Although interventions on the feedback loop have been implemented by removing neurons and back propagating gradients, a generalizable multi-purpose interpretability is still far from reach. 

Deep neural networks require a large amount of labeled training data to reach reliable conclusions. For instance, the system needs to observe the driver’s behavior at the red light to be able to learn to stop at red light both in a sunny and rainy weather, both in daylight and in night, both in fog and in snow, and so on. This causes a significant overhead in labelling every possible situation. Hence, our aim is to build an explainable machine learning system that can learn the meaning of “red light” and use this knowledge to identify many other related situations, e.g. although red light may look different in darkness vs daylight, the most important aspect in such a situation is to identify that the vehicle needs to stop. In other words, we would like to transfer the explainable behaviour of a decision maker to novel situations.

In summary, we would like to develop an end-to-end trainable decision maker operating in sparse data regime with an integrated interpretability module. Our main research direction to build such a system is two folds: learning representations with weak supervision and generating multimodal explanations of classification decisions. 

Further Information

For further information see Zeynep Akata's Website.


Zeynep Akata studied computer science at Trakya University, Turkey (2008), she holds a MSc degree from RWTH Aachen, Germany (2010) in informatics and a PhD degree from University of Grenoble, France (2014). After completing her PhD at the research institute INRIA Rhone Alpes with Prof. Dr. Cordelia Schmid, she worked as a post-doctoral researcher at the Max Planck Institute for Informatics in Saarbrücken between 2014-2017 with Prof. Dr. Bernt Schiele and was a visiting researcher with Prof Trevor Darrell at UC Berkeley.

Between 2017 and 2019, she was an Assistant Professor with the University of Amsterdam in the Netherlands, and Scientific Manager of the Delta Lab, where she worked on basic principles of "Deep Learning“. Additionally, she was a Senior Researcher at the Max Planck Institute for Informatics in Germany.


Prof. Dr. Zeynep Akata
Explainable Machine Learning

University Tübingen
Cluster of Excellence "Machine Learning"
Maria-von-Linden-Str. 6, 4th floor
Room No. 40-30/A24
72076 Tübingen

+49 7071 2970890
zeynep.akataspam prevention@uni-tuebingen.de