Leveraging Conceptual Models of Trust in Automated Systems to Promote `Appropriate Trust' in Autonomic Systems
Bibliographic record
Abstract
There are subtle differences between automated and autonomic software systems with respect to the observability of information that supports trust between the system and its operators (designers, administrators, and users). Applying conceptual models of trust developed for human relationships with automated systems directly to autonomic systems is simply inadequate. An autonomic system can, however, be modeled as a composition of sub-level automation systems. In this manner, designers can engender 'appropriate trust' in autonomic systems using conceptual models developed for automated systems, but must consider how administrators and users will observe information required for trust, specifically: purpose, process, and performance. It is possible to provide designers, administrators and users with information to engender trust by using an abstraction hierarchy modeling framework (for example: ecological interface design). In this paper, we propose the use of goal models during early requirements engineering to describe the purpose information of a system in an abstraction hierarchy.
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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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".