When AIs become oracles: generative artificial intelligence, anticipatory urban governance, and the future of cities
Bibliographic record
Abstract
Abstract Generative Artificial Intelligence (AI) is boosting anticipatory forms of governance, through which state actors seek to predict the future and strategically intervene in the present. In this context, city brains represent an emerging type of generative AI currently employed in urban governance and public policy in a growing number of cities. City brains are large-scale AIs residing in vast digital urban platforms, which manage multiple urban domains including transport, safety, health, and environmental monitoring. They use Large Language Models (LLMs) to generate visions of urban futures: visions that are in turn used by policymakers to generate new urban policies. In this paper, we advance a twofold contribution. Theoretically, we develop a critical theory of anticipatory governance in the age of generative AI. More specifically, we focus on technocratic approaches to anticipatory governance, to explain how the act of governing extends into the future by means of predictive AI technology. Our approach is critical in order to expose the dangers that the use of AI (generative AI, in particular) in urban governance poses, and to identify their causes. These dangers include the formation of a policy process that, under the influence of unintelligible LLMs, risks losing transparency and thus accountability, and the marginalization of human stakeholders (citizens, in particular) as the role of AI in the management of cities keeps growing and governance begins to turn posthuman. Empirically, we critically examine an existing city brain project under development in China and ground our critical theory in a real-life example.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".