{
  "id": 8300,
  "url": "https://doi.org/10.1145/3173574.3174014",
  "title": "Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Making",
  "summary": "Calls for heightened consideration of fairness and accountability in algorithmically-informed public decisions-like taxation, justice, and child protection-are now commonplace. How might designers support such human values? We interviewed 27 public sector machine learning practitioners across 5 OECD countries regarding challenges understanding and imbuing public values into their work. The results suggest a disconnect between organisational and institutional realities, constraints and needs, and",
  "authors": "Michael Veale, Max Van Kleek, Reuben Binns",
  "category": "research",
  "topics": "bias-fairness,children-education,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2018-04-20T00:00:00.000Z",
  "fetched_at": "2026-07-14T16:33:37.176Z",
  "source_slug": "openalex",
  "source_name": "OpenAlex",
  "source_homepage": "https://openalex.org",
  "ethics_ai_record_url": "https://ethics.ai/record/8300",
  "original_url": "https://doi.org/10.1145/3173574.3174014",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}