Infusing Human Factors into Algorithmic Crowdsourcing
Bibliographic record
Abstract
Algorithmic Crowdsourcing (AC) is an emerging field in which computational methods are proposed to automate cer- tain aspects of crowdsourcing. A number of AC methods have proposed recently in an attempt to address this problem. However, existing AC approaches are based on highly simplified models of worker behaviour which limit their practical applicability. To make efficient utilization of human resources for crowdsourcing tasks, the following tech- nical challenges remain open: Fairness of the solution, temporal changes in behaviour, optimizing wellbeing, and non-compliance by users. For AI researchers to propose effective solutions to these challenges, labelled datasets reflecting various aspects of human decision-making related to task allocation in crowd-sourcing are needed. We construct an anonymized dataset based on player behavior trajectories captured by a multiagent game platform - Agile Manage. It allows players to demonstrate their task delegation strategies under different scenarios based on key characteristics involved in crowdsourcing task allocation. The game adopts implicit human computation in which players contribute data which are valuable for research through informal games.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".