Mapping relevant data collection mechanisms for AI training

Oecd

No 48, OECD Artificial Intelligence Papers from OECD Publishing

Abstract: When developing AI systems, practitioners often focus on model building, while sometimes underestimating the importance of analysing the diverse data collection mechanisms. However, the diversity of mechanisms used for data collection deserves closer attention since each of them has different implications for AI developers, data subjects, and other rights holders whose data has been collected. This policy paper maps the principal mechanisms currently used to source data for training AI systems and proposes a taxonomy to support policy discussions around privacy, data governance, and responsible AI development.

Keywords: AI systems; AI training; artificial intelligence; data collection (search for similar items in EconPapers)
Date: 2025-10-03
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:oec:comaaa:48-en

Access Statistics for this paper

More papers in OECD Artificial Intelligence Papers from OECD Publishing
Bibliographic data for series maintained by ().