The definition of good facilitation has to come from the people who facilitate. OFL is built so that the definition they produce, the evaluations and the knowledge, stays a shared resource they own and govern, rather than data a platform extracts.
Collective intelligence, not extraction
Today, when a facilitator helps train an AI, their expertise disappears into a black box they will never own. OFL is built on the opposite model, closer to a data cooperative like MIDATA in health care, where members own and govern what they produce.
The evaluations facilitators write become the standard the field’s AI facilitators are measured against, and that standard stays one facilitators steward, own, and share in the value of, rather than expert judgment sold on by a marketplace they do not control.
We are early, and the governance is still being built. The direction is set: the people who define good facilitation should own that standard, not rent it back from a platform.
How facilitators contribute
- Review the evaluations. Judge real facilitator turns, good or weak, in plain language; those judgments become the per-stage criteria every AI facilitator is measured against. The calibration tool for this is in development.
- Curate the library. Maintain the patterns, research, and definitions in the knowledge base so the field’s shared reference stays accurate and current.
- Author or adapt a method spec. Bring a method you carry into the open registry, or refine the wording of one already there.
Related
- Method specs: the open, forkable standard, mapped to the field rather than walled off from it.
- Evaluation frameworks: how good facilitation is made inspectable and measurable, per stage of each method.