Rough AI truth score
92/100
Stevenson's case-based indictment is strongly grounded: capital punishment and serious criminal cases are vulnerable to wrongful conviction, official misconduct, weak defense, racial inequality, and the punishment of children and disabled people. Narrative cases do not estimate prevalence by themselves, but independent exoneration and court records substantiate the principal mechanisms.
Based on three central claims · high confidence
Documented exonerations establish the phenomenon beyond dispute and identify long delays between conviction and correction. Registry totals are lower bounds rather than prevalence estimates because discovery, investigation, legal access, and the definition of exoneration constrain which cases become visible.
Exoneration case coding and specialist reviews repeatedly identify these contributing factors, often in combination. Recorded factors depend on available documentation and legal findings, so their observed frequencies should not be treated as precise causal shares of all convictions.
Counsel, investigation, review, and humanizing information can change legal outcomes and expose error, while accountability supports deterrence and correction. Proximity is an ethical and professional orientation rather than a standalone causal intervention, and structural reforms require resources, rules, and enforcement.
What this number means. It is an AI-generated first-pass judgment of three central factual or causal claims—not a rating, exhaustive fact-check, or human peer review. Claim credits are 100% for supported, 75% for mostly supported, 50% for mixed, and 25% for weak, then averaged and rounded. Lower confidence means the score should move more as better evidence arrives.
Method three-central-claims/0.1.0 · checked 2026-09-01 · 3/3 selected claims assessed · method and source audit