Bibliographic record
Abstract
Abstract Respect for patient autonomy and data privacy is generally accepted as one of the foundational Western bioethical values. Nonetheless, as our society embraces expanding forms of personal and health monitoring, particularly in the context of an aging population and the increasing prevalence of chronic diseases, questions abound how artificial intelligence (AI) may change the way we define or understand what it means to live a free and healthy life. Drawing on different use cases of AI health monitoring, this book explores the socio-relational contexts that frame the promotion of AI health monitoring, as well as the potential consequences of such monitoring for people’s autonomy. It argues that the evaluation, design, and implementation of AI health monitoring should be guided by a relational conception of autonomy, which addresses both people’s capacity to exercise their agency and broader issues of power asymmetry and social justice. It explores how interpersonal and socio-systemic conditions shape the cultural meanings of personal responsibility, healthy living/aging, trust, and caregiving. These norms in turn structure the ethical space within which expectations regarding predictive analytics, risk tolerance, privacy, self-care, and trust relationships are expressed. Through an analysis of home health monitoring for older and disabled adults, direct-to-consumer health monitoring devices, and medication adherence monitoring, this book proposes ethical strategies at both the professional and systemic levels that can help preserve and promote people’s relational autonomy in the digital era.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.047 | 0.022 |
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".