{
  "annotation_id": "c1f1db0e-e1a1-4fb8-a216-d3fcde22fc7c",
  "date_created": "2026-02-17T05:53:24.162000Z",
  "date_modified": "2026-02-17T05:53:24.162000Z",
  "file_hash": "49af3695b7504d379a4f24334cbadb0178f83882c4b4194bd4398f91f9b507f6",
  "private": false,
  "record": {
    "abstract": "This essay examines how judicial review should adapt to address challenges posed by artificial intelligence decision-making, particularly regarding minority rights and interests. As I argue in this essay, the rise of three trends-privatization, prediction, and automation in AI-have combined to pose similar risks to minorities. Here, I outline what a theory of judicial review would look like in an era of artificial intelligence, analyzing both the limitations and the possibilities of judicial review of AI. I draw on cases in which AI decision-making has been challenged in courts, to show how concepts of due process and equal protection can be recuperated in a modern AI era, and even integrated into AI, to provide for better oversight and accountability, offering a framework for judicial review in the AI era that protects minorities from algorithmic discrimination.",
    "arxiv_id": "2601.09757",
    "authors": [
      "Sonia Katyal"
    ],
    "categories": [
      "cs.CY",
      "cs.AI"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "title": "Democracy and Distrust in an Era of Artificial Intelligence",
    "url": "https://arxiv.org/abs/2601.09757",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "584d644d-e6df-4a44-8b94-6da5bc30ecd9",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}