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Digital Information Ecology: A Terminological Framework for the Lifecycle of Knowledge Within Large Language Model Training Corpora

Year: 2026

Type: article

Abstract: Priority registration for the concept of Digital Information Ecology within the AUGMANITAI framework (Ehstand 2025–2026). Author ORCID: 0009-0006-3773-7796. Digital Information Ecology designates the systematic study of how information units enter, persist, mutate, interact, and decay within LLM training corpora, treated as dynamic ecosystems. Bridges currently siloed literatures on machine unlearning (Bourtoule et al. 2021), dataset documentation (Gebru et al. 2021), and temporal knowledge degradation (Cheng et al. 2024) into a unified ecological vocabulary. Full definitions maintained in the unpublished AUGMANITAI Compendium. ISO 704 / ISO 1087 / ISO 30042 compliant.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.18727996

Author: Ehstand, Andreas

Language: English

Topic: Ethics and Social Impacts of AI

Subfield: Safety Research

Field: Social Sciences

Domain: Social Sciences

Open Access status: green

OpenAlex ID: W7130935631

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