Bibliographic record
Abstract
Abstract “Digital ageism” is a new form of ageism that is embedded into technology and AI systems. Building on the World Health Organization’s recently published policy brief entitled “Ageism in AI for Health”, our work draws attention to digital ageism referring to the nexus of ageism (discrimination or bias related to age) that is mediated and perpetuated by artificial intelligent (AI) systems and technologies. This paper presents a conceptual framework for identifying various cycles of injustice that perpetuate ageism into our digital spheres. This framework also identifies key contributors that need to be addressed in order to break these cycles. We present the results of a scoping literature review, informed by Arksey and O’Malley, to identify the mechanisms in which digital ageism can be introduced as demonstrated in the literature. This work contributes to foundational knowledge about age-related biases in AI and how they might be encoded or amplified in AI systems. This work sheds light on the need for ethical thinking and other approaches to better include older people, and ensure technology benefits the 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 | 0.011 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".