{
  "id": 11297,
  "url": "https://arxiv.org/abs/2607.15202v1",
  "title": "Self-Evolving Human-Centered Framework for Explainable Depression Symptom Annotation",
  "summary": "Annotation quality is a major bottleneck in building reliable and explainable artificial intelligence (XAI) systems for mental health research. In depression-related datasets, labels are often assigned without structured evidence, symptom-level justification, or traceable alignment with the criteria of the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, Text Revision (DSM-5-TR), limiting both transparency and downstream model interpretability. We propose a self-evolving, ex",
  "authors": "Hoang-Loc Cao, Van Pham, Truong Thanh Hung Nguyen, Phuc Truong Loc Nguyen, Phuc Ho, Veronica Whitford, Hung Cao",
  "category": "research",
  "topics": "safety-alignment,healthcare,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-16T16:59:54.000Z",
  "fetched_at": "2026-07-17T05:10:53.887Z",
  "source_slug": "x-arxiv-cs-hc",
  "source_name": "arXiv cs.HC",
  "source_homepage": "https://arxiv.org/list/cs.HC/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/11297",
  "original_url": "https://arxiv.org/abs/2607.15202v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}