Situated Visual Alarm Displays Support Machine Fitness Assessment for Nonexplainable Automation
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
Determine if situated visual alarm displays can support machine fitness assessment (MFA), facilitating improved hazard recognition and alarm accuracy assessment in the presence of inaccurate alarms. Poor performance of opaque automation is more difficult to detect, which increases the likelihood of cascades resulting in overall system failure. MFA reduces the negative impact of poor automation performance. Integrated alarm visualizations were shown to 32 nurses for 10 cases focused on patient outcome and 17 focused on alarm quality, all using real patient data. Five of the ten outcome cases would ultimately result in an emergency (unbeknownst to the nurse). Alarm cases ended with a true, false, or unnecessary alarm. Responses for nurses’ concern, confidence, alarm quality, and intended response were recorded. Qualitative analysis of interviews was performed. Using the situated visual alarm displays, nurses reported less confidence (6.5 vs. 9.1, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</i> < 0.001), more concern (5.4 vs. 1.6, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</i> < 0.001), and more urgent responses for emergency cases. Their alarm event detection was better than the alarms’ detection (0.608 vs. 0.438, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</i> < 0.001), as was their interpretation accuracy (0.453 vs. 0.243, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</i> < 0.001). Nurses showed differentiated concern for emergency cases, nonemergency cases with alarms, and those without alarms (5.4 vs. 3.8 vs. 1.6, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</i> < 0.001). Situated visual alarm displays combining visual trends with alarm signals improves detection of hazardous events and mitigates the negative effects of poor opaque automation performance.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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