Dynamic Weighted Majority Approach for Detecting Malicious Crowd Workers
Bibliographic record
Abstract
Crowdsourcing is a paradigm that utilizes human intelligence to solve problems that computers cannot yet solve. However, the introduction of human intelligence into computations has also resulted in new challenges in quality control. These challenges originate from the malicious behaviors of crowd workers. Malicious workers are workers with hidden motives, who either simply sabotage a task or provide arbitrary responses to attain some monetary compensation. Recently, many studies have tried to detect and reduce the impact of malicious workers. The mechanisms vary from using ground truth to peer review by experts. Although the use of such mechanisms may increase the overall quality of outputs, it also imposes overhead costs in terms of money and/or time, with such costs being often remarkable and contradictory to the philosophy of crowdsourcing. In this paper, a novel dynamic weighted majority method is introduced to detect malicious workers based on a new malicious metric. Effectiveness of the proposed methodology is then showed by presenting the experimental results.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".