Using administrative and judicial remedies under data protection laws to challenge the processing of sensitive personal data by international sport governing bodies
Bibliographic record
Abstract
Abstract Increasingly, international sport governing bodies (SGBs) are processing the sensitive personal data of athletes in order to regulate issues relating to ‘fair play’ in sport, including doping and the eligibility of intersex and transgender athletes. Athletes, human rights groups, scholars, and journalists have raised concerns that this data processing violates the rights of athletes under data protection laws—particularly, the European Union (EU)’s General Data Protection Regulation , which has broad application due to its territorial scope. Although some SGBs have taken steps to incorporate international human rights into their rules, it is unlikely this will provide athletes with a legal basis to challenge the data processing activities of these SGBs. As a result, it remains necessary for athletes to use EU and national data protection laws to challenge the practices of SGBs that violate their data protection rights. Yet, the pathway for athletes to use these laws to enforce their rights is somewhat unclear and untested due to mandatory arbitration clauses that presumptively require athletes to bring their disputes to the Court of Arbitration for Sport (CAS), instead of national courts, and CAS choice of law rules that may prevent the application of EU and many national data protection laws in CAS proceedings. The purpose of this paper is to examine the administrative and judicial remedies available to athletes under EU and national data protection laws to challenge the practices of SGBs. With respect to administrative remedies, a recent privacy complaint filed in Canada against the World Anti-Doping Agency for its disclosure of doping control data to SGBs to administer sex-based eligibility regulations, will be discussed. With respect to judicial remedies, the ability of athletes to bring their privacy complaints to the national courts of EU member states, instead of CAS, will be discussed in light of the recent decision of the Grand Chamber of the Court of Justice of the EU in International Skating Union v. Commission and the case of NADA Austria and Others that is before the Court of Justice of the EU.
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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 | 0.073 | 0.160 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.015 | 0.021 |
| Scholarly communication | 0.022 | 0.010 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.018 | 0.017 |
| 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".