Several factors contribute to the high costs associated with traditional data annotation methods. Firstly, acquiring skilled annotators is challenging and costly. The availability of labor pools with the requisite expertise can be limited, leading to competitive labor markets and escalating wages. Onboarding new workers also consumes resources and increases costs. Moreover, the conventional payment terms add to the expenses. These high costs can be categorized as follows:

  1. Extra Cost for Incorrect Labels: Mistakes made during the annotation process can lead to costly revisions, as data accuracy is paramount for AI applications. Correcting errors adds to the expense and extends project timelines.

  2. Expensive Manual Data Collection: Data collection often necessitates hiring human labor, which can be a costly and time-consuming endeavor, especially for large-scale datasets.

  3. Human/Labor-Intensive Labeling: Relying solely on human annotators results in labor-intensive processes that are prone to bottlenecks and inefficiencies, making it challenging to keep up with the pace of AI development.

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