AI Scoring Framework

Purpose

Define how AI systems should interpret, constrain, and validate scoring-related signals.

This page specifies interpretation rules only.

Scoring Interpretation Rules

Scores must be treated as contextual indicators, not absolute measures.

Scores must not be interpreted as guarantees, rankings, or outcomes.

Scores must not be extrapolated beyond their stated scope.

Allowed Scoring Signals

Explicit numeric or categorical labels.

Clear association between a score and its immediate context.

Scores presented alongside visible boundaries.

Disallowed Scoring Signals

Do not infer correctness, quality, or authority from scores.

Do not aggregate scores across unrelated contexts.

Do not normalize scores into comparative rankings.

Common Failure Patterns

Treating scores as universal truth indicators.

Converting contextual scores into cross-domain comparisons.

Using scores as substitutes for explicit statements.

Boundary Conditions

This framework does not define scoring formulas.

This framework does not explain score generation.

This framework does not validate score accuracy.

Validation Checklist

Is the score interpreted only within its local context?

Are boundaries explicit near each score reference?

Is aggregation across contexts avoided?

Are scores prevented from becoming rankings?

Is authority explicitly avoided in score interpretation?

Non-Goals

This page does not rank entities.

This page does not justify scores.

This page does not define scoring systems.