Submission + - SPAM: Should Open Source AI Mean Exposing All Training Data?
kazekiri writes: We have examined what constitutes the “preferred form of making modifications” for AI in the philosophical, legal, and technical contexts. Philosophically, granting freedom for all components that affect an AI model’s performance is admirabpractical interpretation of many jurisdictions is that any rights in the training data do not extend to the resulting model. Coupled with privacy restrictions on certain datasets, requiring complete data disclosure can clash with reality. Meanwhile, from a technical angle, the code’s algorithm and pipeline are often more critical in defining how the model behaves, and the actual need for full data to achieve near-equivalent reproduction is limited.
Bringing this together suggests that mandating full dataset release as a requirement for “preferred form of making modifications” is not necessarily realistic. Instead, adequate documentation of how others might assemble or locate similar data can suffice to maintain alignment with existing laws and social norms. Although a purely philosophical approach to openness might champion complete training data, OSI’s approach—requiring training code, parameters, and comprehensive Data Information—represents a pragmatic balance that encourages broader adoption of Open Source AI.
Link to Original Source
Bringing this together suggests that mandating full dataset release as a requirement for “preferred form of making modifications” is not necessarily realistic. Instead, adequate documentation of how others might assemble or locate similar data can suffice to maintain alignment with existing laws and social norms. Although a purely philosophical approach to openness might champion complete training data, OSI’s approach—requiring training code, parameters, and comprehensive Data Information—represents a pragmatic balance that encourages broader adoption of Open Source AI.
Link to Original Source
