Assessing compliance with UK loot box industry self-regulation on the Apple App Store: a longitudinal study on the implementation process
Bibliographic record
Abstract
Loot boxes in video games can be purchased with real-world money in exchange for random rewards. Stakeholders are concerned about loot boxes’ similarities with gambling and their potential harms (e.g., overspending money and developing gambling problems). The previous Conservative UK Government decided to first try relying on industry self-regulation to address the issue, rather than to impose legislation. These self-regulations have since been published by the industry trade body, Ukie (UK Interactive Entertainment). Responding to many stakeholders’ desire for a transparent and independent assessment of their implementation, we assessed companies’ compliance with three empirically testable measures and also whether the rules were actively enforced. The 100 highest-grossing iPhone games were longitudinally examined both prior to the self-regulations coming into effect on 18 July 2024 (i.e., between January and June 2024) and after to check for potential improvement (i.e., between July and December 2024). Disappointingly, widespread non-compliance and non-enforcement were observed. Amongst games with loot boxes, none (0.0%) sought to obtain explicit parental consent prior to enabling loot box purchasing by under-18s. Only 23.5% disclosed loot box presence, and the few disclosures were all visually obscured and difficult to access. A mere 8.6% consistently disclosed the probabilities of obtaining different rewards for all loot boxes found. The rules were not enforced, contrary to Ukie’s promise: all of the games that were non-compliant before the self-regulations came into effect remained non-compliant many months later, despite Ukie and the Apple App Store having been provided with evidence of the contraventions and put on notice to delist those games if remedial actions were not forthcoming. Platforms (e.g., app stores), the advertising regulator, and the consumer protection regulators must better enforce pre-existing rules to ensure adequate consumer protection as already promised. Video games and loot boxes are no longer novel; laws that apply to all industries must also be enforced against this one. Governments are advised against relying on industry self-regulation, especially after repeated demonstrations of its many failings. Stricter legal regulation of loot boxes should be adopted. Preregistered Stage 1 protocol: https://doi.org/10.17605/OSF.IO/3KNYB (date of in-principle acceptance: 25 March 2024).
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".