Evaluability AI
The missing layer beneath AI evaluation
Evals tell you how good it is.
Evaluability tells you
whether you can rely on it.
The Book
R‑BED™
Evaluable AI
Risk-Based Evaluability Design (R‑BED)™:
Building Governable AI Systems
Vishal Srivastava, PhD
Tanmay Sah, PhD
Risk-Based Evaluability Design (R-BED) is a framework for building AI systems that can be evaluated in proportion to the risk they carry — so that when someone asks “prove it,” the proof exists.
Why quality scores alone cannot support governance, audit, or regulatory decisions
How to design systems whose evidence is reproducible, traceable, and defensible over time
A risk-tiered approach: matching the depth of evaluability to the stakes of the decision
The Position Paper
Governance runs on evidence, not scores. This position paper introduces evaluability — the capacity to produce and validate decision-ready evidence for AI governance — and the principle that reorganizes everything downstream: evidence is never sufficient in the abstract, only relative to the decision it must support.
Foundations of Evaluable AI • Version 1.0 • 2026
Vol. 1 • Foundational Essay Series
Evidence you can trust. Not just scores.
Issue 1 — July 2026
Foundational Essay
A passing score is a snapshot. Governance runs on evidence that survives scrutiny and time. Issue 1 defines evaluability — and why the score may pass today, but the evidence must hold tomorrow.
Read Issue 1 →One essay per issue on evaluability, decision-grade AI evidence, and governing AI systems you can actually rely on. No noise.
By Vishal Srivastava, PhD