Bibliographic record
Abstract
Today, technologies described as artificial intelligence (AI) impact older adults in numerous and diverse ways, yet the field of gerontology has not seriously engaged AI governance. From algorithmic decision making used in health insurance, employment, housing, and public benefits, to the information ecosystem, to AI companions, AI applications implicate important issues of ethics, service access, and ageism. Policy levers could help address these issues and mitigate harm, but there is currently a paucity of federal AI regulations. As AI and algorithmic harms are better understood, the call from public interest groups and researchers in various fields has gotten louder for comprehensive AI and data privacy policy in the United States. Yet there has been little attention on how rapid development and deployment of AI may affect older adults, and their diverse interests are not well represented in discourses about AI policy making or governance. Drawing on a narrative synthesis of academic and policy literature and attending to the principles in the Blueprint for an AI Bill of Rights, we explain how these principles can guide the development of ethical governance of AI to ensure accountable development and implementation to promote the interests of older adults.
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 | 0.051 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.073 |
| Scholarly communication | 0.020 | 0.028 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.016 | 0.016 |
| Insufficient payload (model declined to judge) | 0.006 | 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".