{"ID":2870682,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.11487","arxiv_id":"2509.11487","title":"Collective Recourse for Generative Urban Visualizations","abstract":"Text-to-image diffusion models help visualize urban futures but can amplify group-level harms. We propose collective recourse: structured community \"visual bug reports\" that trigger fixes to models and planning workflows. We (1) formalize collective recourse and a practical pipeline (report, triage, fix, verify, closure); (2) situate four recourse primitives within the diffusion stack: counter-prompts, negative prompts, dataset edits, and reward-model tweaks; (3) define mandate thresholds via a mandate score combining severity, volume saturation, representativeness, and evidence; and (4) evaluate a synthetic program of 240 reports. Prompt-level fixes were fastest (median 2.1-3.4 days) but less durable (21-38% recurrence); dataset edits and reward tweaks were slower (13.5 and 21.9 days) yet more durable (12-18% recurrence) with higher planner uptake (30-36%). A threshold of 0.12 yielded 93% precision and 75% recall; increasing representativeness raised recall to 81% with little precision loss. We discuss integration with participatory governance, risks (e.g., overfitting to vocal groups), and safeguards (dashboards, rotating juries).","short_abstract":"Text-to-image diffusion models help visualize urban futures but can amplify group-level harms. We propose collective recourse: structured community \"visual bug reports\" that trigger fixes to models and planning workflows. We (1) formalize collective recourse and a practical pipeline (report, triage, fix, verify, closur...","url_abs":"https://arxiv.org/abs/2509.11487","url_pdf":"https://arxiv.org/pdf/2509.11487v2","authors":"[\"Rashid Mushkani\"]","published":"2025-09-15T00:39:59Z","proceeding":"cs.HC","tasks":"[\"cs.HC\",\"cs.CY\"]","methods":"[\"Diffusion Model\"]","has_code":false}