{
  "id": 4551,
  "url": "https://arxiv.org/abs/2605.10604v1",
  "title": "Fairness vs Performance: Characterizing the Pareto Frontier of Algorithmic Decision Systems",
  "summary": "Designing fair algorithmic decision systems requires balancing model performance with fairness toward affected individuals: More fairness might require sacrificing some performance and vice versa, yet the space of possible trade-offs is still poorly understood. We investigate fairness in binary prediction-based decision problems by conceptualizing decision making as a multi-objective optimization problem that simultaneously considers decision-maker utility and group fairness. We investigate the ",
  "authors": "Mieke Wilms, Christoph Heitz",
  "category": "research",
  "topics": "bias-fairness,finance-investment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-11T14:04:27.000Z",
  "fetched_at": "2026-07-14T16:31:03.581Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/4551",
  "original_url": "https://arxiv.org/abs/2605.10604v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}