Understanding common challenges arising from human labelers
Before we dive into the best practices of labeling accuracy and consistency, we will define common challenges we must tackle through our labeling framework. Labeling inaccuracy and ambiguity are generally triggered by one or more of the following seven causes:
- Poor instructions: Labeling inconsistencies will arise from unclear or insufficient instructions for the data annotation task. If annotators are not given clear guidelines, they may make assumptions or guesses that lead to inconsistent or inaccurate annotations.
- Human bias: Bias can introduce ambiguity when the data is skewed toward a particular result or outcome, leading to inaccurate interpretations. A common solution is to assign multiple annotators to label the same data, choosing the most frequently occurring label as the correct one. However, this aggregation or voting method can sometimes exacerbate bias rather than rectify it. For instance, if the...
