{
  "id": 3033,
  "url": "https://arxiv.org/abs/2607.10915v1",
  "title": "Normative Alignment of Recommender Systems via Internal Label Shift",
  "summary": "We introduce NAILS (Normative Alignment of Recommender Systems via Internal Label Shift), a simple and scalable method for aligning recommendation outputs with target distributions over item-level attributes, such as categories. Recommender systems optimized solely for user engagement often fail to satisfy broader normative objectives, including fairness, diversity, and editorial values. NAILS modifies the user-conditional item distribution to induce a specified marginal distribution over attrib",
  "authors": "Johannes Kruse, Kasper Lindskow, Michael Riis Andersen, Ryotaro Shimizu, Julian McAuley, Pierre-Alexandre Mattei, Jes Frellsen",
  "category": "research",
  "topics": "bias-fairness,safety-alignment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-12T20:43:22.000Z",
  "fetched_at": "2026-07-14T16:11:46.979Z",
  "source_slug": "x-arxiv-fairness-query",
  "source_name": "arXiv fairness query",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/3033",
  "original_url": "https://arxiv.org/abs/2607.10915v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}