{
  "id": 8908,
  "url": "https://doi.org/10.3390/electronics10050593",
  "title": "Evaluating the Quality of Machine Learning Explanations: A Survey on Methods and Metrics",
  "summary": "The most successful Machine Learning (ML) systems remain complex black boxes to end-users, and even experts are often unable to understand the rationale behind their decisions. The lack of transparency of such systems can have severe consequences or poor uses of limited valuable resources in medical diagnosis, financial decision-making, and in other high-stake domains. Therefore, the issue of ML explanation has experienced a surge in interest from the research community to application domains. W",
  "authors": "Jianlong Zhou, Amir H. Gandomi, Fang Chen, Andreas Holzinger",
  "category": "research",
  "topics": "healthcare,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2021-03-04T00:00:00.000Z",
  "fetched_at": "2026-07-14T16:33:47.094Z",
  "source_slug": "openalex",
  "source_name": "OpenAlex",
  "source_homepage": "https://openalex.org",
  "ethics_ai_record_url": "https://ethics.ai/record/8908",
  "original_url": "https://doi.org/10.3390/electronics10050593",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}