{
  "id": 18350,
  "url": "https://arxiv.org/abs/2608.10194",
  "title": "Context and Symmetry in Auditing: A Case Study of Skeleton Inference in Motion Capture",
  "summary": "arXiv:2608.10194v1 Announce Type: new Abstract: Humans are increasingly expected to interact with AI systems that observe and make inferences about them - but do these systems actually work? A standard approach to answering this question is AI auditing. Conducting an AI audit requires identifying how a system behaves (i.e., determining what types of inputs to audit it with and then observing and documenting actual system behavior) and contrasting that with how a system should behave (i.e., deter",
  "authors": "Emma Harvey, Emanuel Moss, Hauke Sandhaus, Abigail Z. Jacobs, Mona Sloane",
  "category": "research",
  "topics": "transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-12T04:00:00.000Z",
  "fetched_at": "2026-08-12T05:10:43.828Z",
  "source_slug": "arxiv-cscy",
  "source_name": "arXiv cs.CY",
  "source_homepage": "https://arxiv.org/list/cs.CY/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/18350",
  "original_url": "https://arxiv.org/abs/2608.10194",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}