# Reliability Scoring Summary

## Purpose
Explain how reliability-aware assessment helps prevent fragile, stale, or low-quality inputs from driving high-impact decisions.

## Audience
- Product and policy owners
- Risk and compliance stakeholders
- Engineering teams implementing governed decisions

## What Reliability Scoring Does
Reliability scoring reduces decision fragility by weighting or gating inputs according to quality, freshness, provenance, consistency, and policy alignment.

It helps answer a practical question:
Should this decision proceed, proceed with additional guardrails, or escalate because the input quality is not good enough?

## Typical Scoring Axes
- Source integrity
- Data freshness
- Completeness
- Consistency against expected patterns
- Validation confidence
- Policy compliance

## Example Threshold Behavior
- High confidence: proceed within declared constraints
- Medium confidence: proceed with additional guardrails, flags, or reduced authority
- Low confidence: escalate for review or deny according to the decision contract

## Governance Implications
- Thresholds should be versioned in the decision contract
- Threshold changes should be promoted through controlled change processes
- Evidence should record the confidence state and reason codes used in the final outcome

## Why It Matters
Reliability-aware scoring helps organizations use AI-assisted inputs without allowing uncertain or low-quality signals to silently control high-impact outcomes.