Privacy by the Wayside: The New Information Superhighway, Data Privacy, and Intelligent Transportation Systems
Bibliographic record
Abstract
For a time, it was common to refer to the internet as the “information highway.” As is common with new technologies, physical infrastructure became a handy metaphor for a complex information system, and in this case the term invoked a parallel between the construction of the national highway systems earlier in the twentieth century and the construction of internet infrastructure at the end of the twentieth century. Although “information highway” has faded as a term for the internet, the metaphor has traveled full circle. Interestingly, the phrase is now an apt description of our literal highways, as roads and cars are increasingly linked by information technologies that enable communication between our cars, road infrastructure, and information communications networks. Intelligent Transport Systems (ITS) integrate vehicles and surface transportation infrastructure with information, communication, and sensory technologies to improve the safety, efficiency, security, service, accessibility, environmental responsibility, and reliability of the transportation system.Significant privacy concerns arise from the collection, retention, analysis, use, or disclosure of personal information within ITS, including intra- and inter-governmental data sharing, the cross-border sharing of data, surveillance, and data profiling.This article discusses the data protection and privacy issues raised by the use of ITS. Privacy with respect to ITS implicates informational privacy, as well as privacy in public spaces.The article begins with an overview identifying the central privacy issues that arise with ITS and provides an introduction to the legal and institutional privacy framework in Canada. This is followed by a closer analysis of Canada’s data protection regimes and their application to ITS. The article concludes that, while ITS may offer significant benefits for safety, security, and environmental sustainability, it also raises considerable informational privacy risks. However, these information privacy risks can be moderated by ensuring that the design and deployment of ITS from the outset not only complies with existing data protection obligations respecting the collection, use, and disclosure of personal information set out in Canadian statutes but also anticipates and addresses user concerns about privacy risks.
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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".