Human-in-the-loop AI, at scale.
From assessing model accuracy and reducing bias to building multilingual datasets and applying specialist knowledge, AI teams need qualified human contributors. Where will they come from, and how do you verify expertise, keep quality high and scale without losing reliability?
Where are AI organisations struggling to find qualified contributors, and how is that affecting model development?
How can you verify contributor skills, knowledge and performance instead of relying on self-reported credentials?
What opportunities exist to engage university students, graduates, researchers and educators across Africa?
How can more diverse human expertise improve AI performance in underserved languages and markets?
What new models of collaboration could give AI companies access to skilled contributors more efficiently and responsibly?
Not a webinar. A conversation.
No sales presentation. A focused hour among leaders facing the same challenges, to swap perspectives, spot opportunities and meet the people worth knowing.
Alex frames the problem: even the best models depend on people to train, evaluate and improve them.
The room takes over. Leaders facing the same challenge compare what's working, from sourcing to quality control.
Groups of three or four, one topic each, so everyone gets a real turn.
Every group shares what landed, then it's open conversation.
Built for AI and talent leaders.
- Leaders responsible for AI research, model evaluation or AI training.
- Data operations leads scaling human-in-the-loop work without losing quality.
- Talent acquisition and workforce strategy leaders building AI teams.
Connecting education, verified talent and the next generation of AI innovation.
Places are limited. Request yours.
Kept small on purpose, so the conversation stays useful.
Back to the form