Data Annotation: A Requirements Engineering for Machine Learning Systems Perspective

Paper i 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.