Self-Evolving Human-Centered Framework for Explainable Depression Symptom Annotation
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
Record details
Published: 16 July 2026
Source: arXiv cs.HC
Category: Research
Topics: Safety & alignment · Healthcare · Transparency
Retrieved: 17 July 2026
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How to cite this record
ethics.ai (16 July 2026), “Self-Evolving Human-Centered Framework for Explainable Depression Symptom Annotation,” evidence record 11297, https://ethics.ai/record/11297 (originally published by arXiv cs.HC).
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