Trust and Reliance on an Automated Combat Identification System
Bibliographic record
Abstract
OBJECTIVE: We examined the effects of aid reliability and reliability disclosure on human trust in and reliance on a combat identification (CID) aid. We tested whether trust acts as a mediating factor between belief in and reliance on a CID aid. BACKGROUND: Individual CID systems have been developed to reduce friendly fire incidents. However, these systems cannot positively identify a target that does not have a working transponder. Therefore, when the feedback is "unknown", the target could be hostile, neutral, or friendly. Soldiers have difficulty relying on this type of imperfect automation appropriately. METHOD: In manual and aided conditions, 24 participants completed a simulated CID task. The reliability of the aid varied within participants, half of whom were told the aid reliability level. We used the difference in response bias values across conditions to measure automation reliance. RESULTS: Response bias varied more appropriately with the aid reliability level when it was disclosed than when not. Trust in aid feedback correlated with belief in aid reliability and reliance on aid feedback; however, belief was not correlated with reliance. CONCLUSION: To engender appropriate reliance on CID systems, users should be made aware of system reliability. APPLICATION: The findings can be applied to the design of information displays for individual CID systems and soldier training.
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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 | 0.006 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".