• Data labeling has several options—automated, semi-automated, and manual—depending on the labeling tool used. Since labeling involves labelers, their potential bias can be reflected in the process.
• Such biases occur due to unclear guidelines on a number of data labeling works which leads to dependency on individual judgment. To identify and prevent potential biases, detailed labeling guidelines must be in place. Labelers must be sufficiently trained using these guidelines so as to minimize room for bias between labelers.