High distinction, more networking: Prof. Ute Schmid named EurAI Fellow

The European Association for Artificial Intelligence (EurAI) is an umbrella organization for scientific associations devoted to work in the field of artificial intelligence. Every year, about 3 percent of the members of the EurAI member societies are named EurAI Fellows. One of this year’s four new Fellows is Dr. Ute Schmid, Professor of Applied Computer Science / Cognitive Systems at the University of Bamberg and head of the Group on Comprehensible AI (CAI; formerly Explainable AI) at Fraunhofer IIS. In this interview, she explains what this honor means to her and her research, and looks back on her research findings of the past few years.

Prof. Ute Schmid, congratulations on being named a EurAI Fellow! What does this recognition mean for your research?

I’m looking forward to the networking meetings that will take place and to exchanging ideas with the other EurAI Fellows. Of course, I’ve known many of them personally or through publications for quite a few years now, but I’m also delighted when new contact and networking opportunities arise. I believe the visibility that being a EurAI Fellow gives you can definitely be seen as a seal of quality. In this respect, naturally it can also have extremely positive effects in the medium term for projects, new project partners and collaborations at Fraunhofer. Being a EurAI Fellow is proof that you have already done many years of successful research in the field of AI.

And what does it mean for you personally?

For me, it’s a great honor and distinction to have been accepted into this group. If you look at the list of EurAI Fellows, you see some truly big names in European and German AI research. In Germany, for example, you have Professor Elisabeth Andre from Augsburg, who works on affective computing, Wolfram Burgard, one of the best-known roboticists in the field of AI, and Bernhard Nebel, who played a key international role in the field of AI planning. One of the founders of the EurAI Fellows program is also from Nuremberg: AI pioneer Wolfgang Bibel, who’s still very active. So I’m in good company.

When we interviewed you for the magazine in 2020, you explained the setup of the Project Group for Comprehensible AI. At that time, the group consisted of you and a postdoc. What has it gone on to achieve since then?

At the start, we received funding from the German Federal Ministry of Education and Research (BMBF) and Stifterverband for an AI Campus project. The objective was to finance the development and implementation of online learning opportunities in the field of AI. We in CAI launched an online course entitled “Explainable AI for Engineering,” for which there is plenty of demand. Overall, we’ve broadened our scope beyond our focus on explainable AI. For example, we look into topics related to the “third wave” of AI, which, in addition to explainability, includes methods of interactivity and especially examines hybrid approaches that combine machine learning and knowledge-based methods. We’ve developed several demonstrators on these topics. The use cases come from the business world, too – we have good contacts with companies in the automotive sector and in digital medicine. In both areas, our focus is on hybrid, explainable and interactive AI for image-based diagnostics. For example, we combine visual explanations with more complex linguistic explanations. We successfully submitted our approaches here to various conferences this year – for example, the Innovative Applications of AI (IAAI-22), which is part of AAAI-22.

We were also fortunate to be involved in a large BMBF project on human-centered AI (hKI-Chemie) and were able to hire another employee for it, Emanuel Slany. He had previously worked at Fraunhofer as a student assistant, and now he’s doing his doctorate in the field of explainable AI.

What’s next for you? Is the project group working on any products or services for industry?

We’re currently working on a joint project, funded by the Bavarian state government, that focuses on AI for SMEs. In the project, we’re collaborating with two SMEs on interactive, explanatory machine learning, primarily in a bid to implement methods for detecting and reducing biases in classifiers. We’re also just beginning another exciting corporate collaboration, in which we identify patterns from unstructured text data using hybrid and interactive incremental AI approaches. This would make it possible to, say, assign customer inquiries in a complex company structure directly to the right person.

In another upcoming project, we will partner with a company to extract structured information from physician referral letters. This is necessary for being able to apply machine learning to recognize specific patterns in connection with certain diagnoses and, subsequently, disease progressions as well – in other words, to implement predictive analytics.

Prof. Schmid, thank you for your time.