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).

翻译：文本到图像扩散模型有助于可视化城市未来，但可能放大群体层面的危害。我们提出集体申诉机制：结构化的社区"视觉错误报告"可触发对模型及规划工作流程的修复。我们(1)形式化定义了集体申诉机制及其实用流程（报告、分类、修复、验证、结案）；(2)在扩散模型技术栈中定位了四种申诉原语：对抗提示、负面提示、数据集编辑及奖励模型调优；(3)构建了综合严重性、数量饱和度、代表性和证据的强制阈值评分体系；(4)对包含240份报告的合成数据集进行评估。提示层修复速度最快（中位数2.1-3.4天）但持久性较差（21-38%复发率）；数据集编辑和奖励调优较慢（13.5和21.9天）但更持久（12-18%复发率），且规划者采纳率更高（30-36%）。阈值为0.12时达到93%精确率和75%召回率；提升代表性可使召回率升至81%而精确率几乎不变。我们讨论了与参与式治理的整合、风险（如过度拟合活跃群体）及保障措施（仪表板、轮值评审团）。