CONCEPT
Data Commons Governance
The application of Ostrom's design principles to the shared pool of training data underlying AI systems — a framework developed through the Mozilla Foundation's collaboration with the Ostrom Workshop to identify institutional arrangements that respect contributor agency without collapsing into privatization or regulatory capture.
The Mozilla Foundation, collaborating with scholars at the Ostrom Workshop at Indiana University, has developed a practical framework for applying Ostrom's design principles to data commons governance. The framework identifies the specific institutional features that data governance arrangements require: clear definitions of the data commons' boundaries (what data is included, who has contributed it, who may access it), congruent rules for data use (different rules for different types of data and different contexts of use), collective-choice mechanisms (processes through which contributors participate in governance decisions), and monitoring systems (methods for tracking how data is used and whether use conforms to governance rules).
In The You On AI Field Guide
The framework responds to the training data question by demonstrating that neither privatization nor state regulation exhausts the available institutional options. The contributors to the training-data commons did not produce their contributions as property, and retroactively imposing property