ai-product-risk-reviewlisted

# AI Product Risk Review Decide what an AI feature may promise, observe, decide, and do before its implementation or launch outruns the product's evidence and recovery capacity. ## Workflow 1. Define the decision, user job, affected parties, business value, non-AI baseline, feature stage, and consequence if the AI is wrong or unavailable. 2. Establish current authority: product specification, data-flow inventory, model/provider route, tool/action contract, permissions, policy, current eval evidence, support capability, unit cost, latency, and launch state. Label absent facts `not_verified`; never infer them from model memory. 3. Read `references/ai-product-risk-systems.md`. 4. Decompose the experience into input/context, inference, output, user interpretation, optional action, downstream effect, feedback, and recovery. 5. Classify autonomy, reversibility, affected-party reach, data sensitivity, misuse potential, failure detectability, and recovery difficulty. Record both intended use and predictable misuse. 6. Design product controls: narrower scope or deterministic path, disclosure and provenance, editable draft, confirmation, permission, preview, bounded action, fallback, undo, appeal/reporting, support trace, and safe degraded state. 7. Specify the evidence obligations and hand them to the applicable `risk-matched-verification-standard`, `engineering-standard`, privacy, and `delivery-standard` owners. Consume their exact evidence; do