Can data be labeled consistently?

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Bias, Fairness & Discrimination Category
Design PhaseInput PhaseModel PhaseMonitor Phase
Can data be labeled consistently?
  • Labeling bias occurs when data labels are inconsistently applied by different annotators, which can affect fairness and model accuracy. This can happen when: Label definitions are unclear.
  • Annotators interpret criteria differently.
  • Subjective judgments influence labeling decisions.

If you answered No then you are at risk

If you are not sure, then you might be at risk too

Recommendations

  • Clarify labeling requirements, ensuring that label definitions are precise and consistent from the start.
  • Train annotators and provide clear guidelines to reduce subjectivity.
  • Review labeling processes: regularly check annotations for consistency and accuracy.