Privacy in the Smart City as a Wicked Problem: The Need for Design Thinking, Digital Architects, and Agile Infrastructure
Bibliographic record
Abstract
The issue of privacy in smart cities is attracting increasing attention, due in part to technological advances that promise to digitize many aspects of urban life. As artificial intelligence (AI), ubiquitous computing, and the internet of things have become part of contemporary discourse, privacy advocates have become increasingly concerned. The prospect of privacy harms recently caused Sidewalk Labs and the City of Toronto to abandon their effort to create a new ‘smart’ neighbourhood. While technologists have also taken an interest in the topic of privacy in smart cities, they have largely focused on informational privacy. This is, of course, a subject of pressing concern in an age of AI and big data. For instance, many machine learning algorithms memorize portions of their input datasets, meaning that the data of individuals can linger in these systems for longer time periods than originally anticipated. However, privacy is not merely about the collection, use, and disclosure of information; it also concerns autonomy, dignity, and the ability of individuals to engage in social relations. This paper argues that privacy in the smart city is, in fact, a wicked problem that cannot be solved by either straightforward application of technological safeguards or community-based models like the data commons. Neighbourhoods, particularly in multi-cultural societies, do not possess a single, monolithic community that can agree on a set of rules for data management. Rather, they exhibit a constantly changing set of sub-communities with their own values, norms, and interests. Given the existence of these sub-communities, privacy advocates should encourage the use of agile systems that can be developed, maintained, and adapted easily. Design thinking can play a major role in enabling sub-communities to refine their own vision. Finally, architecture played a major role in the development of privacy norms, and it should once again have a prominent role in addressing privacy concerns in the smart city.
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 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 | 0.035 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.061 |
| Scholarly communication | 0.026 | 0.041 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.010 | 0.015 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".