I subscribe to quite a lot of newsletters, covering topics that interest me. But it’s difficult to find the time needed to filter out which newsletters merit a closer look. I remembered reading that Azeem Azhar solves this problem by telling an AI what kinds of things he is looking for, giving it a week’s worth of newsletters, and asking it to give him a concise summary of what might interest him. So I followed his example. I gave NotebookLM background information about Anapoly AI Labs, and asked it for a detailed prompt that would flag anything in my newsletters that might suit a diary post.
When I used that prompt, one of the points it picked up was that our transparency framework ties in well to the EU AI Act, which has a section on transparency requirements That prompted me to look into the UK’s approach. I learned that, instead of regulations, we have regulatory principles for the guidance of existing regulatory bodies such as the Information Commissioner’s Office or Ofcom. The principles cover:
- Safety, security & robustness
- Appropriate transparency and explainability
- Fairness
- Accountability and governance
- Contestability and redress
It occurred to me that some of the experimentation in our labs could focus on these aspects, individual labs being set up to specialise on one aspect, for example. Equally, we might explore how the regulatory princples could form the basis for a quality assurance framework applicable to business outputs created with AI involvement. This will become an important consideration for small businesses and consultancies.
A final thought: any enterprise doing business in the EU must comply with the EU AI Act. That alone could justify a focused lab setup. We might simulate how a small consultancy could meet the Act’s transparency and accountability requirements when using general-purpose AI tools, modelling practical compliance, not just reading the rules. This, too, might merit some experimentation by Anapoly AI Labs.
Transparency Label Justification. This post was drafted by Alec Fearon. Newsletter filtering was supported by NotebookLM as a separate exercise. ChatGPT was used to revise wording and clarify structure. All reflections and framing are human-authored.