Why does an AI system make a certain decision and how can the decision-making process be explained so that a human understands it? These questions are a focus of research at AIML and are of central importance for the understanding and further development of AI systems. Felix Friedrich is doing his doctorate at AIML and has identified a need for improvements in terms of fairness and bias in AI systems. He was able to show that many AI models, such as image generators, tend to reproduce stereotypical ideas, such as a banker as a man in a suit or the nurse as a woman.

Removing prejudices from AI models completely? Probably an impossible endeavour. Ethical considerations are a central component of Friedrich's research. The researcher therefore appeals to human pragmatism: Risks are to be expected with humans, for example in road traffic. It is therefore wrong to expect machines to never make mistakes. AI systems are heavily reliant on human feedback – and the better we understand how the machine works and where the errors lie, the better we understand the potential of AI systems to improve them.