LEVERAGING AN “ETHICAL BY DESIGN” APPROACH TO CREATE MORE INHERENTLY RESPONSIBLE TECHNOLOGY TO SUPPORT AGING
Bibliographic record
Abstract
Abstract Responsible technologies are technologies that appropriately support the needs, abilities, and values of the people using them. While many aspects of responsible technology are ubiquitous, there are a multitude of considerations that are specific to older adults. These age-related aspects must be properly understood and incorporated into the technology development and selection process if technology is to be inclusive of and useful to older adults. However, as many of these aspects are subjective, qualitative, and/or dynamic, developers often consider them to be too abstract, undefined, or difficult to address, resulting in their omission from the technology creation process. In this presentation we will discuss how we can leverage the concept of ‘Ethical by Design’ to drive the change in thinking and doing that is required to enable developers to engage with age-related concepts in technology design, development, and evaluation. Concepts such as user-centered design, values-based engineering, brave spaces, and co-creation will be presented as some of the ways we can directly access lived experiences and translate them into the creation of technologies that are more inclusive of older adults.
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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 | 0.082 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.009 | 0.041 |
| Scholarly communication | 0.019 | 0.016 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".