{
  "id": 9889,
  "url": "https://doi.org/10.1145/3789495",
  "title": "Interpretable Clustering: A Survey",
  "summary": "In recent years, much of the research on clustering algorithms has primarily focused on enhancing their accuracy and efficiency, frequently at the expense of interpretability. However, as these methods are increasingly being applied in high-stakes domains such as healthcare, finance, and autonomous systems, the need of transparent and interpretable clustering outcomes has become a critical concern. This is not only necessary for gaining user trust but also for satisfying the growing ethical and ",
  "authors": "Lianyu Hu, Mudi Jiang, Junjie Dong, Xinying Liu, Zengyou He",
  "category": "research",
  "topics": "safety-alignment,healthcare,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-01-16T00:00:00.000Z",
  "fetched_at": "2026-07-14T16:34:04.096Z",
  "source_slug": "openalex",
  "source_name": "OpenAlex",
  "source_homepage": "https://openalex.org",
  "ethics_ai_record_url": "https://ethics.ai/record/9889",
  "original_url": "https://doi.org/10.1145/3789495",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}