Aging, artificial intelligence, and the built environment in smart cities: Ethical considerations
Bibliographic record
Abstract
Increasingly, artificial intelligence (AI) is being utilized in urban planning and integrated into the built environment (BE) of urban centres, creating 'smart cities' (SC).However, the ethical and legal implications of this trend for the growing elderly population in urban areas are often overlooked.While AI-supported SC may offer resource-efficient management and services for older adults, they also risk excluding a significant portion of this demographic.This paper addresses ethical concerns for older adults in AI-supported SC, drawing from an ethics perspective that combines traditional ethical principles (beneficence, non-maleficence, autonomy, justice) with AI ethics (explicability, transparency).Three examples of non-healthcare SC-AI-BE interactions are provided, aiming to generate ethical discussions within the gerontechnology field.The paper concludes with suggested avenues for empirical research and ethical deliberation.
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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 | 0.031 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.043 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".