{
  "id": 9715,
  "url": "https://doi.org/10.1007/s11063-025-11732-2",
  "title": "Recent Emerging Techniques in Explainable Artificial Intelligence to Enhance the Interpretable and Understanding of AI Models for Human",
  "summary": "Recent advancements in Explainable Artificial Intelligence (XAI) aim to bridge the gap between complex artificial intelligence (AI) models and human understanding, fostering trust and usability in AI systems. However, challenges persist in comprehensively interpreting these models, hindering their widespread adoption. This study addresses these challenges by exploring recently emerging techniques in XAI. The primary problem addressed is the lack of transparency and interpretability in AI models ",
  "authors": "Daniel J. Mathew, Deborah Ebem, Anayo Chukwu Ikegwu, Pamela Eberechukwu Ukeoma, Ngozi Fidelia Dibiaezue",
  "category": "research",
  "topics": "safety-alignment,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2025-02-07T00:00:00.000Z",
  "fetched_at": "2026-07-14T16:34:00.819Z",
  "source_slug": "openalex",
  "source_name": "OpenAlex",
  "source_homepage": "https://openalex.org",
  "ethics_ai_record_url": "https://ethics.ai/record/9715",
  "original_url": "https://doi.org/10.1007/s11063-025-11732-2",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}