Compute precision under noisy annotators

Q: Compute precision under noisy annotators

This question evaluates understanding of statistical performance metrics and label-noise propagation by requiring computation of precision, recall, and F1 for binary labels under known annotator sensitivity/specificity and majority-vote aggregation, in the Statistics & Math domain.

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Two-Annotator Labeling Policy: Precision, Recall, F1, and Generalization

You have two independent annotators who review videos and label them as "illegal" or "legal."

Sensitivity (true positive rate per annotator): s = 0.80
False positive rate per annotator: f = 0.02 (so specificity = 0.98)
Base rate (prevalence of illegal videos): p = 0.01

Policies to evaluate:

At-least-one (OR): Flag a video as illegal if at least one annotator says "illegal."
Both (AND): Flag a video as illegal only if both annotators say "illegal."

Tasks:

For the OR policy, compute precision (PPV) and recall (TPR).
For the AND policy, compute PPV and TPR.
Compute the F1 score for each and state which policy has the higher F1. Show your calculations.
Generalize formulas for n annotators with majority vote (threshold t = ceil((n+1)/2)) and arbitrary p, s, f.

Compute precision under noisy annotators

Two-Annotator Labeling Policy: Precision, Recall, F1, and Generalization

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Compute precision under noisy annotators

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Two-Annotator Labeling Policy: Precision, Recall, F1, and Generalization

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