{
  "id": 4243,
  "url": "https://arxiv.org/abs/2606.12434v1",
  "title": "Pluralistic-Alignment Urbanism: Operationalizing a Right to AI for Inclusive Public Space",
  "summary": "Municipal agencies increasingly use machine learning to inventory sidewalks, score streetscapes, and generate visualizations of public-space interventions. These systems produce outputs that enter budgeting, design iteration, and public justification, yet judgments about inclusion, safety, and belonging remain contested. This paper proposes Pluralistic-Alignment Urbanism (PAU), a procedural governance framework that treats public-space AI systems as civic infrastructure and formulates a procedur",
  "authors": "Rashid Mushkani",
  "category": "research",
  "topics": "regulation,safety-alignment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-15T23:59:37.000Z",
  "fetched_at": "2026-07-14T16:30:50.574Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/4243",
  "original_url": "https://arxiv.org/abs/2606.12434v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}