{
  "id": 17081,
  "url": "https://arxiv.org/abs/2608.05800v1",
  "title": "Validity, Reliability, and Transparency in Artificial Intelligence Regulation",
  "summary": "Artificial intelligence (AI) systems increasingly mediate decisions affecting individuals and societies. Existing data protection frameworks address certain privacy-related harms, particularly those arising from data leakage, re-identification, and profiling. However, they inadequately capture a more fundamental risk: unreliable or unjustified inference produced by AI systems even when data collection and processing are legitimate. This article argues that modern AI raises distinct concerns of c",
  "authors": "A. Mukundan, Debayan Gupta, Subhashis Banerjee",
  "category": "research",
  "topics": "regulation,privacy-surveillance,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-06T09:36:15.000Z",
  "fetched_at": "2026-08-07T05:10:58.501Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/17081",
  "original_url": "https://arxiv.org/abs/2608.05800v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}