{
  "id": 5348,
  "url": "https://arxiv.org/abs/2604.23615v1",
  "title": "Applications of the Transformer Architecture in AI-Assisted English Reading Comprehension",
  "summary": "This paper studies interpretable and fair artificial intelligence architectures for understanding English reading. Introduced transformer-based models, integrating advanced attention mechanisms and gradient-based feature attribution. The model's lack of interpretability, reduction of algorithmic bias, and unreliable performance in learning environments are the current issues faced in natural language teaching. A unified technical pipeline has been constructed, including adversarial bias correcti",
  "authors": "Ping Li",
  "category": "research",
  "topics": "bias-fairness,safety-alignment,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-04-26T09:11:38.000Z",
  "fetched_at": "2026-07-14T16:31:40.222Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/5348",
  "original_url": "https://arxiv.org/abs/2604.23615v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}