Responsible AI & Governance
A practical grounding in responsible AI, built on the NIST AI Risk Management Framework, the EU AI Act, and the OECD and UNESCO principles.
From principles to practice
Everyone agrees on the principles. The work is in applying them. Read enough AI policy documents and the same handful of ideas keep appearing: AI should be safe, fair, transparent, accountable, and respectful of privacy. The OECD AI Principles say it. The UNESCO Recommendation on the Ethics of AI says it. Company AI policies say […]
Risk tiers: how much governance is enough?
Not every use case deserves the same scrutiny Treating an AI meeting-notes summariser with the same ceremony as an AI credit-scoring system wastes everyone’s time — and treating them the same in the other direction is dangerous. The insight behind the EU AI Act is that governance should scale with risk. The tiering idea Unacceptable […]
Assessing a system before you buy it
Most AI in an organisation arrives through procurement Far more AI enters a business inside purchased software than gets built in-house. It arrives as a feature in an HR platform, a scoring tool in a CRM, an assistant bolted onto a helpdesk — and it usually arrives without anyone asking the questions they would ask […]
Incidents: what to do when it goes wrong
Assume it will, and decide now what happens AI incidents are not exotic. A chatbot tells a customer something untrue about their rights. A scoring tool is found to work poorly for one group. A summarisation tool omits a safety warning. Generated content is published containing invented facts. Each of these has happened somewhere, and […]
The questions worth asking every time
Responsible AI is a habit, not a document Most organisations write principles and then struggle to connect them to Tuesday. The connection is a short set of questions asked routinely — in the meeting where someone proposes a tool, not in an annual review. The six Who is affected by this, and were they asked? […]