AI training data workflow vary and are built on algorithms. But they are trained on data created and evaluated by humans.
Without standardized processes AI systems can produce unpredictable
When data work is unstructured, the effects flow directly into AI systems.
The IntelliScan Standards Institute advances the global standardization of artificial intelligence data practices by developing professional frameworks, certifications, and governance models that ensure the quality, integrity, and reliability of AI training data. Through the Artificial Intelligence Data Body of Knowledge (ADBOK), the Institute equips organizations and professionals with the knowledge, standards, and workforce capabilities required to build trustworthy AI systems at scale.
To become the world’s leading authority for artificial intelligence data standards, certification, and workforce development, shaping a global ecosystem where high-quality training data, ethical human-in-the-loop processes, and rigorous evaluation practices enable trustworthy and responsible AI systems that benefit society.
ADBOK provides the professional framework for building, evaluating, and governing AI training data. It establishes shared terminology, certification pathways, and governance standards.
Target Role: Entry-level annotator
Primary Competency Focus: Follow guidelines, escalate uncertainty, and maintain confidentiality
Target Role: Senior annotator / reviewer
Primary Competency Focus: Handle edge cases, conduct calibration, and support quality control
Target Role: Operations manager / QA lead
Primary Competency Focus: Direct workflows, manage quality, and prepare for audit
Target Role: Data engineer / alignment specialist
Primary Competency Focus: Integrate pipelines, implement RLHF, and monitor deployment
