# Evidence Weight **Entity class:** Uncertainty And Evidentiary Layer concept **Evidence Weight** is a concept in the uncertainty and evidentiary of the counterterrorism predictive-graph ontology. Evidence Weight matters because the analytic object is a changing, partially observed system rather than a finished dossier. ## Counterterrorism predictive-graph role Its controls are part of the computational trust fabric, not peripheral paperwork: permissions, provenance, purpose, and auditability must remain machine-enforceable across systems. The analytic state should distinguish ground truth, observed evidence, inferred state, and predicted state. ## Relationships - **domain router:** [[wiki/Uncertainty Quantification|Uncertainty Quantification]]. - **ontology neighbors:** [[wiki/Chain of Custody|Chain of Custody]] and [[wiki/Confidence Level (Intelligence Analysis)|Confidence Level (Intelligence Analysis)]]. - **synthesis:** [[wiki/Counterterrorism Predictive Graph|Counterterrorism Predictive Graph]] and [[wiki/Predictive Intelligence Loop|Predictive Intelligence Loop]]. ## Conversational-AI Context [[articles/Epistemic Invariance in Conversational AI|Epistemic Invariance in Conversational AI]] extends evidence weighting into conversational systems, where a claim's cultural category, speaker framing or alleged causal agent must not change the procedure used to classify support, contradiction and uncertainty. ## Simple Reminders, Quotations, and Thoughts > “A scientifically disciplined conversational AI should not change its standard of evidence merely because a claim arrives dressed as religion, mental illness, conspiracy theory, technology, spirituality, or political dissent.” > **— Bryant McGill**, *Epistemic Invariance in Conversational AI, 2026* [[reminders/Scientific Literacy/One Standard of Evidence for Every AI Claim by Bryant McGill|One Standard of Evidence for Every AI Claim by Bryant McGill]] ## Sources - [NIST/SEMATECH — e-Handbook of Statistical Methods (accessed September 23, 2026)](https://www.itl.nist.gov/div898/handbook/) - [Oden Institute — Tan Bui-Thanh research profile (accessed September 23, 2026)](https://oden.utexas.edu/people/directory/Tan-Bui/) - [[articles/Epistemic Invariance in Conversational AI|Epistemic Invariance in Conversational AI]] — application to conversational AI and invariant evidence classification. **As of:** 2026-09-23