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, ...| Leon Furze
In this post, I run through the end-to-end workflow that now underpins almost all of my writing. I show how analogue note-taking (pocket notebooks, fountain pens) and spoken drafting (iPhone Voice Memos → Otter/Whisper transcription) feed into successive passes through Claude for clean-up, ChatGPT o3 for link-insertion and live research, and finally a quick HTML export for one-paste publishing in WordPress. Along the way I weigh the productivity gains against the environmental, ethical and ...| Leon Furze
This article, part of a series updating "Teaching AI Ethics," explores the environmental impact of artificial intelligence, particularly generative AI. It emphasizes the need for transparency in AI's energy usage, highlights the resource-intensive nature of training and using AI models, and prompts educational discussions on sustainable technology practices.| Leon Furze