Paul Hünermund

Project title

Optimizing Human-AI Interaction: Integrating Domain Knowledge into Causal AI Systems

What is your project about?

AI is changing the way industries work, but many systems still struggle to be fair, reliable, and transparent. A new type of AI—called Causal AI (or cAI)—is being developed to help solve these problems. Unlike traditional AI, which looks for patterns in data, cAI tries to understand the actual causes and relationships behind events. What makes it unique is that it relies on human experts to guide its thinking, helping it make better and more balanced decisions.

But here’s the catch: we still don’t know much about how people actually work with cAI or how their input affects the AI’s decisions. This project explores how experts share their knowledge with cAI systems and what impact this has. The goal is to create simple, practical guidelines to help people and AI work together more effectively and responsibly.

How did you become interested in your particular field of research?

For data analysis, I was always drawn to one big question: how can we tell if a policy or business strategy really works? During my PhD, I focused on evaluating whether subsidies actually boost innovation and competitiveness in companies—something that plays a vital role in economic growth and societal progress. But I soon realized that figuring out what really causes what isn’t easy. Once you start thinking about cause and effect, you notice it everywhere: Are two glasses of red wine actually good for your health? Does playing Mozart to your baby in the womb really make a difference? These kinds of questions led me to causal AI—a fascinating new field focused on helping machines reason about cause and effect, just like humans try to do.

What are the scientific challenges and perspectives in your project?

Our project tackles one of the biggest challenges in AI today: helping machines understand cause and effect, not just spot patterns. This kind of reasoning is at the heart of how humans make decisions—and it’s what’s missing in most current AI systems. As AI becomes more common in business strategy and entrepreneurship, the next big leap is Causal AI—technology that can think more like people do. We’re exploring how expert knowledge and mental models—the kinds of intuitive frameworks experienced professionals use—can help AI deliver smarter, fairer, and more trustworthy insights. By combining ideas from economics, behavioral science, and machine learning, we’re working to build tools that allow people and AI to make better decisions together—especially when the stakes are high.

What is your estimate of the impact, which your project may have to society in the long term?

In the long run, this project has the potential to make a real difference—both in Denmark and around the world. It tackles a major challenge in the development of Causal AI: how to make sure these systems not only work well but also work fairly and transparently with human input. By bringing together experts from fields like business strategy, computer science, and behavioral science, we’re helping build smarter, more trustworthy AI. In Denmark, the project supports CBS’s leadership in human-centric AI research and helps position the country as a global hub for this fast-growing field. Most importantly, we aim to make an impact beyond academia—by sharing what we learn with businesses, researchers, and students alike.

Which impact do you expect the Sapere Aude programme will have on your career as a researcher?

This project brings together two key areas of my past research—AI algorithms and technology management—which makes it a natural next step in my career. I see it as the beginning of a promising research journey that bridges technical innovation with insights from the social sciences. In a field often driven by complex methods, I hope to offer a fresh perspective that highlights the human and strategic dimensions of AI. On a personal level, this grant gives me a long-term opportunity to grow at Copenhagen Business School, one of Europe’s top business schools, and to strengthen my profile as an internationally recognized expert in Causal AI.

Background and personal life

I’m originally from Germany and studied economics at the University of Mannheim, HEC Lausanne, and New York University. I earned my Ph.D. in business economics from KU Leuven in Belgium. In September 2020, I moved to Denmark to join Copenhagen Business School. Before that, I spent three years as an assistant professor at Maastricht University in the Netherlands. My research has been featured in international media outlets including Harvard Business Review, The Economist, The Wall Street Journal, MIT Sloan Management Review, Politiken, Frankfurter Allgemeine Zeitung, Süddeutsche Zeitung, and Neue Zürcher Zeitung. In 2021, I was named one of the “Top 40 Under 40” by Capital, a leading German business magazine. Outside academia, I hold a black belt in taekwondo and enjoy playing the harmonica and the 4-string tenor banjo—though I admit the latter sometimes tests my wife’s patience.