While artificial intelligence (AI) systems, such as home assistants, search engines or large language models like ChatGPT, may seem nearly omniscient, their outputs are only as good as the data on which they are trained. However, ease of use often leads users to adopt AI systems without understanding what training data was used or who prepared the data, including potential biases in the data or held by trainers. A new study by Penn State researchers suggests that making this information available could shape appropriate expectations of AI systems and further help users make more informed decisions about whether and how to use these systems.

A study from the Cluster of Excellence "The Politics of Inequality" at the University of Konstanz examines whether diversity in the classroom leads to greater tolerance among school students. The findings reveal that this is not the case for every classroom constellation.