Participatory Design of Affective Technology: Interfacing Biomusic and Autism
Bibliographic record
Abstract
The benefits of user-centered and participatory design have been widely acknowledged for the development of technologies that are likely to be appropriated by the product’s stakeholders. While participatory design has been applied to some affective technologies, the technical and algorithmic complexity of those based on semi-intelligent information filters (SIIFs) pose distinct challenges. Coincidentally, these technologies raise important and distinct ethical issues that make stakeholder input critical during product design. We present a framework for fostering genuine engagement from stakeholders through the case example of biomusic - a SIIF-based affective technology that translates emotion-related physiological changes into sound. During a 3-day workshop, ethnographic methods were used to collect data about the interface between biomusic and individuals on the autism spectrum. From these data, emergent themes, such as such as privacy, data security, conceptions of assistive technology and representation of emotions were analyzed using a grounded theory approach. In order to illuminate distinct design decisions implicated by these complex and interwoven ethical issues, we propose a design framework consisting of a technological, a human-centered and an ecological lens. This framework and recommendations provide a concrete praxis for engaging stakeholders in the complex issues associated with the design of SIIF-based emotion-oriented systems.
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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 | 0.032 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| 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".