Glossary · DPP in the company: ownership and trust
AI in DPP work
Also known as: AI for digital product passports, AI-assisted DPP, AI in passport data preparation
AI in DPP work is the use of AI systems to help prepare and maintain digital product passport data, for example by extracting values from documents, mapping them to a data model, classifying materials, detecting gaps and inconsistencies, preparing audience-specific summaries and translating. It supports people; it must not invent product facts.
- EU
- DPP
- Data governance
In one sentence
AI can extract, map, check, summarize and translate DPP data, but must not invent product facts; human review and preserved sources stay required.
Example
An AI tool extracts substance data from 400 supplier declarations and flags 12 inconsistencies; a materials engineer reviews the flagged entries and approves the values, and each value keeps a link to its source document.
How it applies
- Good fits: Extraction from certificates and declarations, mapping to the passport data model, classification, gap and inconsistency checks, audience summaries, translation.
- Hard limit: AI must not fill missing values with plausible guesses. A gap is reported as a gap and sent to the data owner.
- Human review: A named person approves AI-prepared values before they enter the passport; see human oversight for the principle in AI regulation.
- Documented use: Record which tool was used for which step, and keep the link from each value to its source so authentication and integrity are preserved.
- Technical documentation: Translated or summarized passport content follows the same review and terminology rules as other published documentation.
Compared with the USA, Canada and China
The same limits apply anywhere AI prepares regulatory evidence. The difference is the legal frame for the AI tool itself: the EU AI Act, voluntary frameworks in the USA and Canada, and China's rules for algorithms and generative AI.
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