Algorithms to the Rescue: Market Mechanisms for Consensual Trading of Unbiased Individual Data
Bibliographic record
Abstract
This paper proposes a novel algorithmic market mechanism to address key challenges in individual data markets. Current data collection practices lack transparency and proper compensation, leading privacy-conscious users to opt out and creating biased data sets. Our proposed mechanism enables an intermediary platform to obtain unbiased samples of individual-level data while appropriately compensating users for privacy loss. Through theoretical analysis and simulations using both synthetic and real-world data sets, the authors demonstrate that their mechanism provides unbiased data samples at near-optimal cost compared with benchmark approaches. The mechanism outperforms both fixed-compensation methods and centralized-optimization approaches, even when platforms have partial information about user privacy preferences. Surprisingly, platforms achieve better outcomes by using this market mechanism rather than relying on estimated privacy preferences from user behavior. The approach is practical to implement, using straightforward sampling and conventional compensation mechanisms rather than complex techniques, like differential privacy. The mechanism enables creation of effective data markets that benefit both data subjects and buyers while ensuring compliance with regulations requiring transparency and consent. The findings are particularly relevant as new privacy regulations emerge globally and third-party tracking faces increased constraints, providing a viable solution for improving data quality and fairness in digital markets.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".