This first instalment in the Teaching AI Ethics 2025 series revisits the theme of bias in generative AI. It explains how data bias, model bias and human bias interact to produce skewed or discriminatory outputs in large-language and image-generation systems, illustrates those problems with up-to-date research and examples, critiques the limitations of current “guard-rail” fixes, and closes with practical ways teachers can embed critical discussions of AI bias across English, Mathematics, ...
