SPIDER: Interplay Assessment Method for Privacy and Other Values
Bibliographic record
Abstract
In the design of many sociotechnical systems, ensuring people's privacy is crucial. Available strategies, patterns, and technologies for ensuring privacy are often associated with drawbacks with respect to other values, such as security, fairness, or safety. Thus, system design entails navigating such value interactions, aiming to find solutions that reconcile privacy and other values. However, no systematic methodology is available for assessing the interplay between privacy and other values in a design. To solve this problem, we propose SPIDER, a methodol-ogy for the systematic assessment of the interplay between privacy and other values. With SPIDER, system design-ers can investigate, quantify, and visualize the type (positive/neutraVnegative) and strength of the interplay between privacy and other values, from different stakeholders' point of view. This helps identify areas where further improvement of the design is needed to resolve tensions between privacy and other values. We demonstrate the application of SPIDER in the domain of Cooperative Connected Automated Mobility (CCAM) on a use case of an automated delivery vehicle.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".