Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Participatory design (PD) for Artificially Intelligent (AI) systems has gained in popularity in recent years across multiple application domains, both within the private and public sectors. PD methods broadly enable stakeholders of diverse backgrounds to inform new use cases for AI and the design of AI-based technologies that directly impact people's lives. Such participation can be vital for mitigating adverse implications of AI on society that are becoming increasingly apparent and pursuing more positive impact, especially to vulnerable populations. This panel brings together researchers who have, or are, conducting participatory design of AI systems across diverse subject areas. The goal of the panel is to elucidate similarities and differences, as well as successes and challenges, in how PD methods can be applied to Artificially Intelligent systems in practical and meaningful ways. The panel serves as an opportunity for the HCI research community to collectively reflect on opportunities for PD of AI to facilitate collaboration amongst stakeholders, as well as persistent challenges to participatory AI design.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it