Performance-based Data-driven Assessment of Trust
Bibliographic record
Abstract
The likelihood of an agent’s success in achieving its goals and subgoals under specific operational constraints is an important factor in performance-based trust. Trust management systems are being proposed to monitor performance and other trust-related system variables to improve operational efficiency. We present in this paper an exploratory study on the feasibility of assessing the performance of drone operators in a target-finding task through subjective and objective evaluations of the drone’s flight data. Thirty-nine participants were trained in using a teleoperated drone to find specific alphanumeric targets arranged on the laboratory floor and thereafter performed the task three times in separate three-minute runs. Following this, they made subjective assessments of other operators’ performance based on viewing the flight paths flown. Linear mixed effects modelling of subjective performance assessments showed either non-significant or weak relationships between subjective assessments and actual operator performance. Binomial logistic regression modelling showed significant relationships between features of the drone’s flight pattern and operator performance. These results could inform the development of data-driven, performance-based trust management systems for use in collaborative human and machine environments.
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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 | 0.008 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".