A vision on the potential of AI in human expert input and labeling in military lessons learned

Publication date
2026-01-20
Document type
Conference paper
Author
de Boer, Maaike H. T.
Eden, Andrew
Eaton, Jacqueline
Lange, Douglas S.
Organisational unit
Department of Data Science, TNO, The Netherlands
Joint Warfare Centre, NATO, Norway
Joint Analysis and Lessons Learned Centre, NATO, Portugal
Naval Information Warfare Center Pacific, San Diego, California, United States
DOI
URI
Conference
Publisher
Universitätsbibliothek der HSU/UniBw H
Book title
Artificial Intelligence in Security and Defense : Proceedings of the workshop AI4SD
First page
17
Last page
24
Is part of
Peer-reviewed
✅
Part of the university bibliography
Nein
File(s)
Language
English
Keyword
Lessons learned
Artificial intelligence
Knowledge management
Human expert input
Labeling
NATO
Abstract
Effective Knowledge Management (KM) is critical for ensuring that the right information reaches the right personnel at the right time. This process begins with the capture and storage of high quality information to avoid the common trap of ‘Garbage in = Garbage out.’ This paper examines the North Atlantic Treaty Organization’s (NATO) Lessons Learned Process, as a KM system that includes a change management process, emphasizing its role in enhancing interoperability and Command and Control within NATO military headquarters. The quality of the input and labeling of the observations and lessons captured, stored, and shared during the process contributes significantly to the likelihood that the Lessons Learned Process will result in tangible and lasting change. Current approaches to human-expert input and labeling do not ensure the necessary quality. This paper explores the potential of Artificial Intelligence (AI) techniques to augment or replace human involvement in these tasks. We present a forward-looking perspective on the integration of advanced AI and Machine Learning (ML) technologies to elevate the quality and effectiveness of the input and labeling of information during the Lessons Learned Process.
Version
Published version
Access right on openHSU
Open access