Improper data curation

Mitigation strategy

1. Implement rigorous, multi-stage data validation and cleaning protocols to identify and rectify structural errors, inconsistencies, and duplicates within the dataset prior to model training. 2. Establish and enforce standardized data labeling guidelines and employ quantitative quality assurance mechanisms, such as inter-annotator agreement (IAA) checks, to minimize human-introduced label errors and subjectivity. 3. Develop a comprehensive data governance framework that mandates clear data ownership, standardized metadata creation to track data lineage and provenance, and regular automated audits to ensure data integrity and compliance with predefined quality standards.