ICE-TEA: International Consortium on Ethics in Technology and Aging:Achievements and Future Directions for AGE-WELL Catalyst Program in Healthy Aging Initiative on Ethical AgeTech
Bibliographic record
Abstract
We live in a rapidly advancing technology-based society, where innovations significantly impact our daily lives, including health and healthcare. AgeTech encompasses technologies such as e-health, robotics, artificial intelligence, and mobile devices to support the health and independence of older adults. While AgeTech offers many benefits, it also presents ethical challenges. Technologies can streamline and economize services but may also disrupt lives. In-home health monitoring systems raise privacy concerns, while the “digital divide” can exacerbate health disparities. Importantly, these ethical considerations are often overlooked or underappreciated by technology researchers and developers. In 2023, we received funding from AGE-WELL, Canada’s Technology and Aging Network (www.agewell-nce.ca), for a one-year catalyst project ICE-TEA: International Consortium on Ethics in Technology and Aging to explore the ethical dimensions of AgeTech and to lay the foundations for a substantive, longer-term research and knowledge mobilization initiative. The key aim was to achieve a stronger ethical focus within the AgeTech sector and ensure ethical thinking is part of the research, design and development of technology from start to finish. The initiative centers around the perspectives of older people to promote ethical design thinking by encouraging and supporting culture change within AgeTech research and industry, promoting the development of more effective, appropriate, and inclusive technologies.
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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 | 0.143 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.023 | 0.012 |
| Open science | 0.006 | 0.028 |
| Research integrity | 0.012 | 0.016 |
| Insufficient payload (model declined to judge) | 0.034 | 0.013 |
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".