Mitigating Safety Risks in Information Systems: A Self-Adaptive Approach
Bibliographic record
Abstract
With various benefits of Information Systems (IS), there are increasing apprehensions regarding their safety implications. Despite researchers’ investigation and publication of the safety advantages of IS, several case studies have revealed distinct, potentially fatal issues and safety risks even with the use of IS. Accidents occur due to differences between the process models (mental models) employed by users and the actual characteristics of the operation. This is particularly true for accidents consisting of interactions between users and safety-critical IS. These users’ process models and Situational Awareness (SA) generate incidents that pose safety hazards to human lives. This ambiguity in the process model and SA may be due to the user’s perspective of what the system does and the actual characteristics of the system. The literature on the user’s process model and SA has been extensively employed in technological systems, but its application to socio-technical systems has been limited. We have identified problems that constitute a potential safety risk with the IS. The issues correspond to the lack of alignment between the process model and situation awareness about the user when interacting with the IS. This lack of alignment and SA can result from various circumstances, including interruptions, multitasking, and cognitive overload. These disruptions make it more challenging to understand the circumstances, which may result in mistakes, inefficiencies, and even safety hazards. Knowing about these interruptions, an information system (IS) could adjust its usage procedure in real time to lessen the disruption’s impact. Such a system could aid in restoring SA by identifying, reacting and adapting to these disturbances. This self-adaptive system (SAS) is used in our study to investigate the impact of an adaptive IS on SA, usability and safety performance outcomes. The findings of this study and the controlled experiment shed light on how system adaptation can mitigate the negative impacts of interruptions, improving safety, effectiveness, efficiency, SA, usability, frustration, and decision-making.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| 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".