What is a fundamental requirement for labeling data in AI?

High quality and standardization are essential for labeling data in AI because they ensure that the labeled data can be reliably and consistently used in training machine learning models. When data is high quality, it reduces the likelihood of errors and biases that could lead to inaccurate model predictions. Standardization facilitates easier comparisons and assessments across different data sets, making it more manageable to evaluate the performance of models and to generalize findings. This is particularly important in AI applications where consistency in labeling directly impacts model performance and decision-making processes. By adhering to high quality and standardized labeling practices, organizations can enhance the effectiveness and reliability of their AI systems.

Quick and cheap processing does not necessarily guarantee effective labeling, as it may compromise quality. Limited sample sizes can lead to inadequate training data, negatively affecting model robustness. Unstructured data handling is a challenge but is not a fundamental requirement for labeling; rather, it pertains to how data can be organized and utilized for labeling purposes.