Data Annotation: A Requirements Engineering for Machine Learning Systems Perspective
Paper in proceeding, 2025
Data annotation, the systematic labeling of raw data (e.g., images, text) [1] , is foundational to the training of machine learning (ML) models, particularly in supervised learning. While data’s importance is clear, the specific processes and requirements for how this data should be annotated, appear inconsistently defined or informal within existing ML software system (MLS) development methodologies [2] . The effective specification of data annotation requirements, the challenges involved, and the traceability from system requirements to annotation activities represent critical considerations in the ML development lifecycle. Understanding these aspects is pertinent for AI/ML engineers and data scientists, requirements engineers, and organizations developing AI solutions.
Author
Yi Peng
University of Gothenburg
Hina Saeeda
Chalmers, Computer Science and Engineering (Chalmers), Interaction Design and Software Engineering
University of Gothenburg
Hans-Martin Heyn
Chalmers, Computer Science and Engineering (Chalmers), Interaction Design and Software Engineering
University of Gothenburg
Jennifer Horkoff
Chalmers, Computer Science and Engineering (Chalmers), Interaction Design and Software Engineering
University of Gothenburg
2025 IEEE 33RD INTERNATIONAL REQUIREMENTS ENGINEERING CONFERENCE, RE
2332-6441 (ISSN)
575-575979-8-3315-2414-2 (ISBN)
33rd International Requirements Engineering Conference-RE-Annual
Valencia, Spain,
Valencia, Spain,
FAMER - Facilitating Multi-Party Engineering of Requirements
FFI - Strategic Vehicle Research and Innovation (2023-00771), 2023-09-01 -- 2026-08-31.
Subject Categories (SSIF 2025)
Computer Sciences
DOI
10.1109/RE63999.2025.00053