The End of Individual Control over Health Information: Promoting Fair Information Practices and the Governance of Biobank Research
Bibliographic record
Abstract
Until recently, rules about protection of health information and the protection of human research subjects largely existed in different universes. In the last decade, we can see a growing awareness of privacy concerns and informational risks in the context of health research. This can be associated with the overall growth of research involving health and other personal data, but also with the increase in genetic research, which raises specific informational risks. Research ethics guidelines and regulations have included more detailed provisions on privacy protection. But they have largely continued to emphasize the importance of informed consent as the main protective mechanism in the context of sharing of health information. At the same time, new health information privacy legislation has in some jurisdictions tried to address the growing informational risks associated with health research. Some statutes have done so by, among other things, incorporating the traditional research ethics mechanism of independent review by Research Ethics Committees or Boards in privacy legislation. In line with a well-established tradition in privacy legislation, these statutes are modeled upon Fair Information Practices but nonetheless allocate a specific role to such Committees in dealing with the issue of consent in the context of research. In this chapter, we make two connected arguments. First, we show how a variety of new developments make it increasingly hard to maintain that we can confidently assure the confidentiality of genetic information and detailed health information in the context of research. These developments include advances in genetic technology -- particularly in the context of personalized genome scanning, the growth of health data sharing websites, the increasing regulatory requirements to publicize research data, and the development of large biobanks. Second, we critically analyze how fair information practices are currently already integrated in some privacy legislation in connection with health research. While we support the use of the concept of fair information practices in the context of research, particularly also because of the increased inability to protect privacy on the basis of informed consent, our analysis suggests that research ethics review has been introduced within some of the privacy statutes without appropriate recognition of the structural weaknesses of the current regulatory regime surrounding research.
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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 | 0.231 | 0.155 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.105 |
| Scholarly communication | 0.032 | 0.029 |
| Open science | 0.005 | 0.024 |
| Research integrity | 0.022 | 0.021 |
| Insufficient payload (model declined to judge) | 0.004 | 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".