Out of Sight, Out of Mind: UI Design and the Inhibition of Mental Models of Security
Bibliographic record
Abstract
In this paper we make the case that UI design inhibits mental models of security by concealing most of the security-relevant aspects of software functionality. Users are frequently required to make decisions that have important security implications, that requires a mental model of software infrastructure to know what actions are ‘safe’ versus ‘unsafe’. People build internal causal models of what they experience that have explanatory and predictive power, and therefore form the basis of the decision-making faculty. By concealing security information, user interfaces hinder the user from building the kinds of models that will keep them safer, and only the small minority who are willing to go beyond the interface will acquire this knowledge. We suggest increasing the visibility of some essential information about the security-relevant aspects of software functionality in a way that ordinary users will be able to make sense of, so that through normal interactions with software everyone develops the kind of knowledge needed to better support security. We review the cognitive science and cybersecurity literature on mental models, present three ‘case studies’ which embody the security concealment problem, and present preliminary suggestions for how UI design might amend this problem.
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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 | 0.011 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".