Studying how AI can measure work fairly.
Our research sits at the intersection of people analytics, occupational health, and machine learning — grounded in real workplaces, with publication and openness in mind.
What we're working on.
Each thread combines field data with methods that stay interpretable, so findings can be questioned and reused.
Performance Measurement
Formula-based KPI scoring for production lines and teams — designing metrics that reward the right behaviour without distorting it.
Workforce Classification
Quadrant and cluster models for understanding headcount, cost, and capability — and the limits of classifying people with data.
Occupational Health
How workplace conditions relate to wellbeing and illness, with survey instruments designed for clarity across languages.
Explainable AI
Keeping models legible: methods that let an operations manager understand and challenge a prediction, not just receive it.
Measurement & Method
Q-methodology and mixed-method instruments for capturing subjective experience rigorously alongside operational data.
AI Pedagogy
How professionals actually learn data and programming — and how interactive, in-browser tools change that.
Research that earns its place in practice.
- 01
Field-first
We study questions that arise in real operations, with data from real organisations.
- 02
Interpretable
If a method can't be explained to the people it affects, it isn't finished.
- 03
Shareable
We build toward open tools and publications others can learn from and build on.
“Good measurement is an act of respect — it takes the work, and the people doing it, seriously.”
Our aim is research that organisations can act on and scholars can scrutinise, without choosing between the two.
Interested in working with us?
We partner with universities, factories, and organisations on applied studies. If a question here resonates, get in touch.
Reach out →