{
  "id": 16956,
  "url": "https://arxiv.org/abs/2608.04474v1",
  "title": "Local Violation Certification for Linear Predict-Then-Optimize Pipelines",
  "summary": "Data-driven decision pipelines combining predictive machine learning models with downstream optimization software are increasingly used to make high-stakes operational decisions. Certifying the safety, fairness, and reliability of these decisions is essential, yet traditional scenario generation methods rely on repeated random testing, which becomes computationally prohibitive when failure events are rare and offers little insight into why failures occur. We present a framework for local violati",
  "authors": "Ş. İlker Birbil, Wenhao Chi",
  "category": "research",
  "topics": "bias-fairness",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-05T05:58:06.000Z",
  "fetched_at": "2026-08-06T05:10:11.148Z",
  "source_slug": "x-arxiv-fairness-query",
  "source_name": "arXiv fairness query",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/16956",
  "original_url": "https://arxiv.org/abs/2608.04474v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}