Human-in-the-Loop AI is the backbone of reliable and trustworthy artificial intelligence systems. This module introduces the fundamental principles of Human-in-the-Loop (HITL) systems and their critical role in the AI lifecycle. You will explore how human intelligence complements machine learning to create more accurate and ethical AI solutions. By understanding the core dimensions of data quality and the high costs associated with errors, you will gain the specialized mindset required for leadership roles in data annotation and quality assurance.
Learning Objectives
By the end of this module, you will be able to:
- Explain what Human-in-the-Loop AI is and why it is essential.
- Describe the different roles humans play across the AI development lifecycle.
- Identify and apply the four key dimensions of data quality: accuracy, consistency, clarity, and completeness.
- Analyze the real-world and financial costs of poor-quality data in AI projects, particularly in an African context.