Vortragende: Prof. Dr. Kristina Yordanova, Greifswald
There are different types of data, such as textual, video, image or sensor data that are used for training artificial intelligence (AI) models. These data are almost always biased in a certain way. Some examples of bias are gender, ethnical or age-related bias. The bias in the data is then integrated in the AI system and it in turn acts and makes decisions according to the data (and bias) it was trained with. As AI, with its sub-field of machine learning (ML) gets more and more popular and AI applications become part of our everyday life, it is very important to mitigate the effect of bias. In this talk I first discuss how bias is introduced in the data and later on in AI systems. I will then illustrate this with some examples of AI systems and how they are affected by gender bias. Finally, I will discuss strategies for mitigating (gender) bias in AI systems.
Ort: Rubenowstr. 3, Hörsaal [EG]
Mehr Infos zur Ringvorlesung hier.