{
  "id": 3059,
  "url": "https://arxiv.org/abs/2607.07762v1",
  "title": "Trustworthy Machine Learning through the Lens of Combinatorial Optimization: Survey and Research Perspectives",
  "summary": "Modern machine learning (ML) increasingly relies on complex models whose behavior is difficult to characterize beyond empirical performance metrics. Across a wide range of tasks, including prediction, generation, and decision-making, models with similar empirical performance can exhibit markedly different properties in terms of their transparency, interpretability, robustness, fairness, privacy, and certifiability. This survey highlights how optimization- and certification-oriented reasoning can",
  "authors": "Thibaut Vidal, Julien Ferry",
  "category": "research",
  "topics": "bias-fairness,safety-alignment,privacy-surveillance,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-08T15:27:30.000Z",
  "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/3059",
  "original_url": "https://arxiv.org/abs/2607.07762v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}