Outcome disparities

https://taxonomy.eticas.ai/risk/bias-outcome-disparities

Risks related to unequal treatment, allocation, or performance outcomes across demographic and protected groups. These are observable differences in what the system does to or for different groups.

Subcategories

Mappings to external frameworks

Status. Each mapping shows what it rests on. Reviewed means a reviewer has confirmed the mapping against the concept’s current definition and the framework edition the taxonomy currently references; when the only confirmation is that of the person who wrote the mapping, it reads Reviewed (author’s confirmation). Outdated means it was reviewed, but the concept’s definition has changed since, so the review has to be repeated. The other statuses mean that no review has been recorded yet. Inherited mappings have been in the taxonomy since before v5.7.0 and are unchanged. They carry no record of who made them or how, and some were checked by hand when the taxonomy first published its mappings. Agent-proposed mappings come from a review carried out by AI agents, which read the framework’s text and the concept’s definition and agreed on the relation; no person has confirmed them yet. Provisional mappings were made without access to the framework’s text. Unreviewed mappings are older mappings whose target or relation was revised in v5.7.0, and any other mapping without a review. Mappings to a superseded edition of a framework are not shown: where the provision continues, they are carried to the current edition. Mappings a review has changed or rejected are not shown.

Standards & frameworks

Framework Reference Status
EU AI Act (Regulation 2024/1689) Annex III — High-risk AI systems referred to in Article 6(2) Agent-proposed
ISO/IEC 42001:2023 — AI Management System A.5.2 AI system impact assessment process Inherited
AIUC-1 — AI Underwriting Company Standard Prevent customer-defined high-risk outputs Inherited
NIST AI Risk Management Framework (AI 100-1) Fair – with Harmful Bias Managed Agent-proposed
NIST AI Risk Management Framework (AI 100-1) MEASURE 2.11: Fairness and bias – as identified in the MAP function – are evaluated and results are documented. Agent-proposed
NIST AI 600-1 — Generative AI Risk Profile Harmful Bias or Homogenization Inherited

Taxonomies & vocabularies

Framework Reference Status
MIT AI Risk Repository Unequal performance across groups Agent-proposed
AIR 2024 Discrimination/Bias Inherited
W3C Data Privacy Vocabulary — AI Extension Data Bias Agent-proposed