The AI didn’t invent the bias. It just paid attention.
There is a particular kind of discomfort that comes from looking at what an AI system has learned and recognising, […]
Messy data isn’t the problem. It’s the symptom.
Every data problem we’ve ever seen in higher education has one thing in common. Somewhere underneath it – underneath the
Data-driven. Are you, though?
“Data-driven” has become one of those phrases that organisations put in strategies, say in meetings, and include in job descriptions
Yes, But What Does It Actually Mean? | No. 1
Relative risk, absolute risk, and why the headlines got HRT badly wrong Let’s start with some fruit. Imagine you have
What data training actually gives you (and it’s not just the skills)
When organisations invest in data training, they tend to measure success in fairly straightforward terms. Can people now use the
How to choose a data visualisation tool for your institution – a framework for data teams
If you work in a higher education data team, there is a reasonable chance your institution is having, has recently
Why data training programmes often fail – and what to do before you commission one
The conversation usually starts with a symptom. Reports that take too long and nobody trusts. Decisions made without data, or
Your HESA return knowledge is probably your institution’s biggest single point of failure
There is a scenario that plays out somewhere in the HE sector almost every year. A small institution is approaching
What the sector is telling me about AI – and why governance alone isn’t enough
Earlier this year I started delivering a series of webinars on AI for AHEP – the Association for Higher Education
Introducing ICE/BERG: an AI literacy framework built to last
There is no shortage of AI guidance right now. Checklists, principles, policy statements, usage rules – they are being produced