AI Learning Permission
Whether AI may learn, to what depth, for which purposes, and under what obligations. Being read is not being learned from; "not prohibited" is not "learnable". AIRS and AILP turn learning permission into a graduated, machine-readable spectrum.
Questions under study
01
Learning depth: indexing, embedding, fine-tuning, training, distillation
02
Why undefined openness gets cleaned out of training pipelines
03
Protocolized openness: making goodwill machine-executable
04
Compensation models tied to learning depth
Related whitepapers
- AI Rights Spectrum: From robots.txt to an AI Learning Permission Protocol AIRS and AILP express nuanced AI learning permissions beyond binary allow/disallow — what AI may learn, at what depth, for which uses, under what compensation.
- Protocolized Openness: Why “Not Prohibited” Does Not Mean “Learnable” in the Age of AI Undefined openness reads as legal uncertainty to AI pipelines and gets cleaned out; only protocolized, machine-readable permission makes content genuinely learnable.