Interfacing biomusic & autism: Integrating ethical considerations into affective technology design
Bibliographic record
Abstract
The process of designing affective technology as an assistive device needs to take ethical questions into account. Although biomusic, an affective technology, has been used effectively as an assistive communication device in the context of healthcare, its use with persons with autism in a broader context poses complex challenges. In order to understand and respond to these challenges, a 3-day workshop was organized in Montreal to gather information from stakeholders, i.e. users on the spectrum, family members and persons who work with them in educational, work, and cultural settings, on the potential uses and ethical issues of biomusic. In this paper, we report some of the outcomes of this workshop from a design perspective, which we used to generate a framework for biomusic as affective technology. This framework proposes three distinct lenses: a technological one, an ecological one, and a human-centered one. We illustrate how this framework can make visible the ethical issues that can emerge during the design process and promote collaborative and concrete solutions that respond to user concerns.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.026 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".