Data cleaning is a foundational task in the AI development lifecycle. The AAIATM Study Guide identifies data quality--ensuring completeness, accuracy, consistency, and correctness--as critical to building effective and unbiased AI systems. Cleaning the data involves removing duplicates, correcting errors, addressing missing values, and standardizing formats.
"Data cleaning is a prerequisite for effective training and evaluation. Poor-quality data leads to inaccurate or misleading model outputs, increasing operational and ethical risks."