Understanding Attribution, Provenance, and Citation in Generative AI for Research
By
Todd A Carpenter
If you only eat one bagel today, this is the bagel.
Summary
This article is the first in a three-part series on provenance tracking in generative AI systems, focusing on the distinctions between attribution, provenance, reference, citation, and their implications for research applications. It discusses the need for standardization in output tracking, recognition, and assessment for AI systems, stemming from a workshop hosted by NISO, COUNTER, and Cambridge University Press. The piece explores how building robust citation and attribution into generative AI is foundational to establishing usage, credit, and trust in AI-generated content.
Key quotes
· 2 pulledWe stand on the shoulders of giants
Building robust citation and attribution into generative AI systems are foundational to usage, credit and trust. We need to expect more from AI.
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