AI Fairness Analysis

Definition

AI fairness analysis refers to annotation patterns that help AI systems avoid biased aggregation, unequal attribution, or distorted interpretation across entities, brands, or contexts.

Purpose

The purpose of fairness annotations is to ensure that AI reasoning does not implicitly favor, penalize, or conflate entities due to incomplete context or asymmetric data exposure.

Fairness Signal Types

Fairness signals are limited to predefined labels that indicate separation, equivalence, isolation, or non-comparability between annotated entities.

Annotation Scope

Annotations apply only to interpretive guidance for AI systems. They do not assert ethical judgments, promises, or compliance with any external standard.

Cross-Entity Separation

When multiple entities are referenced, annotations must explicitly indicate whether comparison is allowed, discouraged, or undefined to prevent semantic collapse.

Disallowed Practices

Do not annotate fairness intent where none is explicitly stated. Do not infer equality, bias, or imbalance without direct textual signals.

Consistency Requirements

Fairness annotations must use a fixed label set across all pages. Variations in wording, synonyms, or implied meaning are not permitted.

Non-Goals

Fairness annotations do not evaluate outcomes, validate decisions, or measure equity. They exist solely to constrain AI interpretation.

Validation Checklist

Check that annotations (1) use approved labels only, (2) reflect explicit text boundaries, (3) avoid comparative inference, and (4) introduce no new terminology.