Bibliographic record
Abstract
Creators of automated systems are increasingly called upon to make their systems transparent. However, contrasting interpretations of the connotation of transparency complicate the systems engineering process. We conducted a scoping review that maps out differences in the automation transparency notion based on origin, historical trends, and operationalization consistency. We discovered that transparency had been used with two opposite meanings in the human-automation interaction literature: seeing-into versus seeing-through, the internal process of automation. The seeing-through connotation embrace consistent objectives and approaches, whereas seeing-into connotation reflect significant divergence in the same dimensions. We demonstrated a variety of transparency design objectives, which regulators can leverage to determine the effectiveness of transparent systems reliably. In addition, we conducted a review of four seeing-into transparency models. This review assists researchers in assessing the suitability of the models for designing transparent systems.Prior studies have shown conflicting results about the impact of seeing-into automation transparency (i.e., information disclosure about automation) on human performance. We conducted an experiment with 24 participants to investigate the impact of transparency in a decision aid guided by a Machine Learning model on human performance measures. The results on state estimation, automation reliance, trust, workload, and self-confidence were not statistically significant. This study showed that disclosing information about a decision aid’s rationale does not necessarily impact operator performance. Given the results of our experiment, we were interested in exploring the effects of seeing-into transparency on task performance with greater statistical power than afforded by any one study through a meta-analysis. However, the lack of a systematic and operational definition of seeing-into transparency intervention complicated the meta-analysis. Therefore, we assessed the complexity of automation transparency intervention and created a logic model that characterized its mechanisms. Finally, we conducted a meta-analysis, a statistical assessment of the magnitude and consistency of the observed effects of automation transparency interventions on task performance. Contrary to the common narrative claims, this meta-analysis demonstrated little evidence that automation transparency is a generalizable principle with regard to its impact on task performance. The effects of automation transparency on trust, situation awareness, and workload remain to be investigated.
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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 | 0.086 | 0.296 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".