Start by clarifying the data format and the definition of 'low-quality annotations' (e.g., items with no majority or high disagreement). Then describe an algorithm that groups annotations by item, computes the majority label, and filters out items that don't meet a confidence threshold, while discussing trade-offs like handling ties and computational efficiency.
Pro tip: Demonstrate awareness of edge cases like ties and the impact of filtering on dataset size and bias; propose a configurable threshold or fallback strategy to show production readiness.
Ask about the data structure (e.g., list of (item_id, annotator_id, label)), the definition of low-quality (e.g., no majority, below confidence threshold), and whether to remove entire items or just annotations.
Propose grouping annotations by item_id, counting label frequencies, and selecting the label with the most votes. Discuss tie-breaking strategies (e.g., random, discard, or use a secondary metric).
Specify a threshold for majority (e.g., >50% or a configurable ratio) to decide which items to keep. Consider also filtering out annotators with low agreement if relevant.
Use a hash map to aggregate votes in O(n) time, then iterate to filter. Discuss memory considerations for large datasets and potential parallelization.
Mention how to validate the cleaned dataset (e.g., compare label distribution, measure inter-annotator agreement) and discuss trade-offs like data loss vs. quality, and potential bias introduced.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is where I started feeling the pressure.
Start by defining a pairwise agreement metric (e.g., Cohen's kappa or raw agreement) between each annotator and the rest of the group, then aggregate into a per-annotator score. Use this score to weight or filter annotations in downstream aggregation, and discuss trade-offs like bias-variance and fairness.
Pro tip: Emphasize that down-weighting should be dynamic and based on statistical significance, not just a fixed threshold, to avoid penalizing annotators who are correct but disagree with a biased majority.
Choose a pairwise agreement measure (e.g., Cohen's kappa, Fleiss' kappa, or raw agreement) that accounts for chance. Ensure it works for your annotation type (categorical, ordinal, etc.).
For each annotator, calculate their average agreement with all other annotators on overlapping items. This yields a peer-agreement score per annotator.
Decide how to map agreement scores to weights: e.g., linear scaling, threshold-based exclusion, or soft weighting via a sigmoid. Consider using confidence intervals to avoid penalizing annotators with few overlaps.
Apply weights when aggregating labels (e.g., weighted majority vote or weighted Dawid-Skene). Ensure the aggregation method supports weights and that the final labels reflect the down-weighting.
Measure impact on label quality using held-out gold data or downstream model performance. Monitor for bias and adjust the weighting scheme as needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Weighting by confidence is conceptually clean but I spent too long second-guessing whether to normalize weights per item or globally.
Start by explaining how to collect confidence scores from annotators, then describe a weighted aggregation method that uses these scores to compute a final label. Emphasize the trade-offs between simplicity and accuracy, and how to validate the approach.
Pro tip: Mention that confidence scores should be calibrated (e.g., via temperature scaling) and that you'd use a held-out set to tune the weighting scheme, showing you think about real-world deployment.
Specify how annotators provide confidence scores (e.g., Likert scale, probabilities) and ensure consistency across annotators.
Select a weighted aggregation scheme (e.g., weighted majority vote, weighted average of probabilities) that incorporates confidence scores.
Address low-confidence annotations, missing scores, and potential annotator bias by setting thresholds or using smoothing techniques.
Evaluate the aggregation on a validation set using metrics like accuracy or F1, and refine weights or method based on performance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Propose a quantitative framework that computes per-annotator disagreement scores (e.g., using pairwise agreement metrics like Cohen's kappa or Krippendorff's alpha) against a consensus or peer majority, then applies a threshold to flag and remove outliers. Discuss how to set the threshold via statistical methods (e.g., confidence intervals, percentile-based) and handle sparse annotators by incorporating uncertainty or using shrinkage estimators.
Pro tip: Emphasize that removal should be a last resort; consider weighting or re-training annotators first, and always validate the impact on data quality and model performance. Also, mention the importance of documenting the process for reproducibility and fairness.
Choose a metric that quantifies an annotator's disagreement with peers across all items, such as average pairwise Cohen's kappa, Krippendorff's alpha, or a model-based approach like Dawid-Skene. Ensure it accounts for chance agreement and is robust to varying label distributions.
For each annotator, calculate their disagreement score relative to the consensus (e.g., majority vote or probabilistic consensus) or to all other annotators. Use only items they annotated, but consider the reliability of the consensus on those items.
Determine a threshold using methods like bootstrapping to estimate the distribution of scores under the null hypothesis of no systematic disagreement, or set it based on percentiles (e.g., bottom 5%) or confidence intervals. Consider the trade-off between removing bad annotators and retaining valuable ones.
For annotators with few annotations, their disagreement scores are noisy. Use shrinkage estimators (e.g., empirical Bayes) to pull scores toward the mean, or set a minimum annotation count below which no removal occurs. Alternatively, flag them for review rather than automatic removal.
After removal, re-evaluate data quality (e.g., inter-annotator agreement) and model performance. Consider simulating removals to assess impact. Iterate on threshold and method as needed, and document decisions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.