Bibliographic record
Abstract
Abstract Artificial intelligence (AI) and AI-mediated decision making currently impact older adults in numerous ways. From algorithmic decision making used in health insurance, employment, housing, and public benefits, to the information ecosystem, to social robots, AI applications implicate important issues of ethics, access, and ageism. The World Health Organization reports that the diverse interests of older adults are not well represented in discourses about AI policy making or governance. As AI harms are better understood, the call from public interest groups and from many academic fields has gotten louder for comprehensive AI and data privacy policy in the United States (U.S.), which lags behind other countries. Drawing from the international insights of European, Canadian, and U.S.-based research, this symposium will consider how older adults and their rights are represented in AI policy documents, laws, and policy discourse. Dr. Ho will discuss ethical concerns resulting from regulatory gaps in direct-to-consumer AI health monitoring platforms in the U.S context. Dr. Stypinska will highlight how European national (Germany, Spain, UK, Holland and Poland) and international policy documents related to AI position older adults. Dr. Gallistl will discuss the relevance of AI explainability in later life, as one of the key terms that currently inform EU AI-governance. Dr. Robillard will review findings and gaps from an analysis of international policies for social robots with aging applications. Participants with and without prior policy knowledge or AI research experience are welcome. Technology and Aging Interest Group Sponsored Symposium
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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 | 0.041 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.051 |
| Scholarly communication | 0.019 | 0.024 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.014 | 0.013 |
| Insufficient payload (model declined to judge) | 0.014 | 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".