SCÖ / Linköping University - Machine Learning Based Services

My employment at the coordination association ends this month. The ESF funding wasn't renewed. Up to eleven of us are being made redundant. The PhD continues, technically, but without the industrial partnership that was supposed to be its foundation.

This is the final post in a series called "The Limits of Making Visible" - not what I was supposed to learn about federated learning, but what I actually learned about technology projects, design practice, and organisational life.

Over eight posts, I've traced an arc from technical promise to organisational reality.

I began by explaining what federated learning is - a genuine technology with real applications, but one that was invoked here for reasons that had little to do with its actual requirements. I explored the service context that vocational rehabilitation operates within, and considered whether mission-oriented framing might have helped or hindered. I documented what federated learning would actually require - data infrastructure, governance frameworks, technical capacity - none of which existed.

Then I described what happened when design work made these gaps visible. Algorithm archaeology at SCÖ revealed the gap between what the Pathway Generator required and what the Swedish context could supply. The silent pivot documented how project milestones were quietly redefined as original goals proved unachievable. And in the limits of making visible, I developed a framework for understanding how organisations respond when design artefacts expose inconvenient truths - from benign correction through co-optive incorporation to adversarial isolation.

What follows is an attempt to synthesise what I've learned.

Federated learning is real. Google uses it for keyboard next word auto-prediction. Hospitals are piloting it for collaborative medical imaging research. The technical papers are sophisticated and the engineering is impressive.