Designing Caring and Informative Decision Aids to Increase Trust and Enhance the Interaction Atmosphere
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
Decision aids have enjoyed extensive use in various domains. While decision aid research and practice have largely focused on making these aids more functional and utilitarian, we propose that one should also purposefully design them as effective interaction partners, especially when one deploys them in contexts that require a “human touch”, such as finance or healthcare. In this paper, we report on the results from an experiment we conducted on the effects that designing caring and informative decision aids have on how users evaluate them and, subsequently, their satisfaction with them. Our results show that using explanations and expressive speech acts can enhance the extent to which users perceive decision aids as informative and caring. These strengthened beliefs subsequently enhance the extent to which users view decision aids as competent and as having integrity and improve the interaction atmosphere, which, in turn, increases users’ satisfaction with their overall interaction with the decision aid. We discuss the study’s contributions to theory and practice.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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