{
  "id": 13804,
  "url": "https://arxiv.org/abs/2607.24275",
  "title": "A Computational Ethical Framework for Financial Digital Phenotyping for Mental Health",
  "summary": "arXiv:2607.24275v1 Announce Type: cross Abstract: Ethical governance of AI-driven systems is often expressed through high-level principles and static documentation, creating a gap between regulatory requirements and system-level verification. This challenge is particularly acute in digital phenotyping, where continuous behavioural data raises concerns around consent, privacy, and fairness. In this paper, we propose a computational ethical framework for AI-driven digital phenotyping system in whi",
  "authors": "Oluwadara Adedeji, Michael Mayowa Farayola, Jeff Brozena, Irina Tal, Regina Connolly, Mark Matthews",
  "category": "research",
  "topics": "bias-fairness,regulation,privacy-surveillance,healthcare",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-28T04:00:00.000Z",
  "fetched_at": "2026-07-28T05:10:12.325Z",
  "source_slug": "arxiv-cscy",
  "source_name": "arXiv cs.CY",
  "source_homepage": "https://arxiv.org/list/cs.CY/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/13804",
  "original_url": "https://arxiv.org/abs/2607.24275",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}