Artificial Intelligence in the City: Building Civic Engagement and Public Trust
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
After navigating various challenging policy and regulatory contexts over the years, in different regions, we joined efforts to create a space that offers possibilities for engagement focused on the expertise, experiences and hopes to shape the future of technology in urban areas. The AI in the City project emerged as an opportunity to connect people, organizations, and resources in the networks we built over the last decade of work on research and advocacy in tech policy. Sharing non-Western and Western perspectives from five continents, the contributors questioned, challenged, and envisioned ways public trust and meaningful civic engagement can flourish and persist as data and AI become increasingly pervasive in our lives. This collection of essays brings together a group of multidisciplinary scholars, activists, and practitioners working on a diverse range of initiatives to map strategies going forward. Divided into five parts, the collection brings into focus: 1) Meaningful engagement and public participation; 2) Addressing inequalities and building trust; 3) Public and private boundaries in tech policy; 4) Legal perspectives and mechanisms for accountability; and 5) New directions for local and urban governance. The focus on civil society and academia was deliberate: a way to listen to and learn with people who have dedicated many years to public interest advocacy, governance and policy that represents the interests of their communities.
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.
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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it