Also known as: data provenance · dataset transparency
Training data provenance is documentation of where the data used to train a model came from, including its sources, licensing status, and any known rights disputes.
Provenance has moved from a research-hygiene concern to a regulatory and legal one, with several jurisdictions advancing rules that would require developers above a threshold to maintain and in some cases publish provenance records.
Documentation does not resolve the underlying copyright questions working through the courts, but it creates a record that makes those questions tractable — which is precisely why positions on mandatory disclosure divide along predictable lines.
AI Governance
AI governance refers to the policies, regulations, and internal controls that determine how AI systems may be developed and deployed, including risk assessment, transparency obligations, and accountability for harms.
Large Language Model
A large language model (LLM) is a neural network trained on vast quantities of text to predict likely continuations of a sequence, producing systems capable of generating coherent language, answering questions, and writing code.
Open-Weight Model
An open-weight model is one whose trained parameters are publicly downloadable, allowing anyone to run, inspect, fine-tune, and deploy it on their own infrastructure without calling an external API.
AI Alignment
AI alignment is the field concerned with ensuring AI systems reliably pursue the goals their operators actually intend, rather than the literal specification given or some proxy the system learned during training.