Bibliographic record
Abstract
As a former agency head explains, antitrust litigation is like fishing: “everybody likes to catch them, but nobody wants to clean them.” Antitrust enforcers around the world are eager to catch digital platforms with monopolization cases, but little attention is being paid to the remedies that will follow. This article examines a new source of complexity for those monopolization remedies — data privacy. In particular, it considers remedies that require access to, or disclosure of the information held by digital platforms, to restore online competition. How are such “data access” remedies impacted by the rise of consumer data privacy law? As the article explains, neither current theory nor past monopolization cases answer this question. Existing theories on the interface between antitrust law and data privacy are focused on liability. Their application may therefore miss the distinct privacy impacts that arise at the remedies stage of a case. Past monopolization cases that ended in data access remedies often ordered disclosure of company, not consumer, information. Individual data privacy was simply not relevant. The rare historical cases that ordered disclosure of consumer information pre-date the rise of U.S. data privacy law from the mid- 1990s to present. For the first time, antitrust remedies may well have to contend with consumer privacy protection, and the control such protection can impart over competitively important data. The article calls for antitrust analysis to consider data privacy in the design of remedies, particularly for digital platforms. Without such analysis, remedies may unwittingly cause privacy harms that outweigh the benefits to consumers from restored competition. A remedy that causes such a reduction in consumer welfare would undermine the purpose of bringing antitrust enforcement action. The article concludes with discussion of two potential approaches for implementing the proposal. The first focuses on obtaining consumer consent to remedial disclosure and use of data. The second focuses on legislative or judicial definitions of data privacy interests that exclude remedial disclosure. Both demand careful consideration of consumer privacy, and the new complexity it creates for monopolization relief.
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 | 0.023 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.032 |
| Scholarly communication | 0.013 | 0.019 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.017 | 0.015 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".