Privacy promises still matter
A new AI use does not erase an earlier confidentiality commitment.
Read the note ↗Research notes
Read the source.
Understand the implication.
Primary-source context for data owners. These notes explain durable issues rather than treating funding announcements or headline transactions as a price list.
Copyright questions need a defined use
The U.S. Copyright Office maintains a dedicated collection of its AI work.
Read the note ↗Fitness for use is a risk-management question
NIST’s voluntary AI framework considers trustworthiness across the AI lifecycle.
Read the note ↗Put the context to work.
Owning a system is not the same as owning its contents
Company systems can contain employee, customer, supplier and licensed third-party material. Rights need to be considered at the dataset and use level.
Read the guide ↗Why removing names does not make data anonymous
Free text, unusual events and combinations of attributes can reveal people even after direct identifiers are removed. Privacy assessment is separate from data-quality assessment.
Read the guide ↗How to compare operational datasets
Compare evidence against the intended use rather than choosing the largest archive or the highest single quality score.
Read the guide ↗