AGEISM IN ARTIFICIAL INTELLIGENCE: A REVIEW
Bibliographic record
Abstract
Abstract Background Artificial intelligence (AI) has emerged as a major driver of technological development in the 21st century, yet little attention has been paid to algorithmic biases towards older adults. "Digital ageism" is a new form of ageism that is embedded into technology and AI systems. Aim: This review aimed to explore how age-related bias is encoded in AI systems to better understand digital ageism. Methods The scoping review follows a six-stage methodology framework developed by Arksey and O'Malley. The search strategy has been established in six databases and we will investigate grey literature databases, targeted websites, popular search engines. An iterative search strategy was used. Studies meet the inclusion criteria if they are in English, peer-reviewed, available electronically in full-text, and included the concepts ‘bias’ and old age. At least two reviewers independently conducted title/abstract screening and full-text screening. Results Our database searches resulted in 7 595 manuscripts that underwent title and abstract screening. Of these 49 papers, were included in the study. The word "ageism" was explicitly mentioned only in about half of these papers. Approximately half the papers mentioned how age-related bias could be encoded into AI systems. The most commonly used AI applicaiton was computer vision. Conclusions Our preliminary findings contribute foundational knowledge about the age-related biases that were encoded or amplified in AI systems. This work advances how AI can be developed in a manner consistent with ethical values and human rights legislation, particularly as it relates to an older and aging population.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".