{
  "id": 5018,
  "url": "https://arxiv.org/abs/2605.02640v2",
  "title": "Trustworthy AI Suffers from Invariance Conflicts and Causality is The Solution",
  "summary": "As artificial intelligence (AI), including machine learning (ML) models and foundation models (FMs), are increasingly deployed in high-stakes domains, ensuring their trustworthiness has become a central challenge. However, the core trustworthy AI objectives, such as fairness, robustness, privacy, and explainability, are hard to achieve simultaneously, especially while preserving utility. This position paper argues that causality is necessary to understand and balance trade-offs in performance an",
  "authors": "Ruta Binkyte, Ivaxi Sheth, Zhijing Jin, Mohammad Havaei, Bernhard Schölkopf, Mario Fritz",
  "category": "research",
  "topics": "bias-fairness,privacy-surveillance,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-04T14:26:28.000Z",
  "fetched_at": "2026-07-14T16:31:26.336Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/5018",
  "original_url": "https://arxiv.org/abs/2605.02640v2",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}