Responsible AI, content provenance, AI image/text detection, LLM-friendly content, model bias, and the ethics of automation.
Sanne is a project manager at a consultancy firm. Since last month, every project has had its own AI knowledge base. This is her working week.
AI image watermarking is useful, but narrower than most claims suggest. Here is what it can and cannot prove.
AI chatbot privacy is not just about prompts. Here is what gets stored, why it is kept, and what users and teams can do about it.
An AI knowledge base per project gives service providers an AI that knows their clients, cases, and conversations — with sources, without noise.
Most organisations already have shadow AI. This 10-minute, 7-question audit shows you exactly where you stand — and what to do next.
AI-generated content is now the default mode for a lot of the web. Here is a workable, honest disclosure standard that respects both the reader and the work.
AI detection is messy, error-prone, and often unfair. Here's how to build an editorial process that catches synthetic content without alienating writers.
AI image generation looks free, but the computational and ethical costs are mounting. Here's what responsible use actually looks like.
