There is a particular kind of discomfort that comes from looking at what an AI system has learned and recognising, in its outputs, something uncomfortably human. Not a glitch. Not a malfunction. Not the robot going rogue. Just a very accurate reflection of the world we gave it to learn from. The algorithm that learned…
Every data problem we’ve ever seen in higher education has one thing in common. Somewhere underneath it – underneath the messy spreadsheets, the unreliable reports, the dashboards nobody trusts, the HESA return that needs three rounds of corrections – there is a governance problem. Not always an obvious one. Not always one that anyone has…
“Data-driven” has become one of those phrases that organisations put in strategies, say in meetings, and include in job descriptions without anyone stopping to ask what it actually means in practice. It has the comfortable quality of sounding rigorous without committing to anything specific. And that, unfortunately, is usually where the trouble starts. Let’s be…
Relative risk, absolute risk, and why the headlines got HRT badly wrong Let’s start with some fruit. Imagine you have a bowl with three lemons and two limes. Lemons are good. Limes, for the sake of this exercise, are not. Someone adds another lime. You now have three limes – a 50% increase. That sounds…
When organisations invest in data training, they tend to measure success in fairly straightforward terms. Can people now use the tool? Can they build the report? Can they run the query? These are reasonable things to want to know. But they miss most of what actually changes. The most significant outcomes from good data training…
If you work in a higher education data team, there is a reasonable chance your institution is having, has recently had, or is about to have a conversation about which visualisation tool to use. There is also a reasonable chance that conversation is either going nowhere, going in circles, or heading toward a decision driven…
The conversation usually starts with a symptom. Reports that take too long and nobody trusts. Decisions made without data, or with the wrong data. A board that asks for better insight and receives dashboards nobody can interpret. Staff who have access to data but lack confidence in using it. A sense, somewhere in the organisation,…
There is a scenario that plays out somewhere in the HE sector almost every year. A small institution is approaching the Student Record return cycle. The person who has managed the return for the last several years is unavailable – on long-term sick leave, has left, is managing a concurrent crisis, or simply cannot remember…
Earlier this year I started delivering a series of webinars on AI for AHEP – the Association for Higher Education Professionals. The attendance has been strong. Consistently strong. And the questions coming in have told me something I think is worth sharing publicly, because it shapes why I’ve built the Responsible AI Toolkit the way…
There is no shortage of AI guidance right now. Checklists, principles, policy statements, usage rules – they are being produced faster than most organisations can read them, let alone implement them. So why add another framework to the pile? Because most of what’s out there has a problem: it’s already going out of date. Guidance…
