Bias & Fairness

https://taxonomy.eticas.ai/risk/bias-fairness

Maturity: established

The risk that an AI system produces outcomes that systematically advantage or disadvantage individuals or groups based on protected or sensitive attributes, leading to unequal treatment, reduced accuracy, or unjust impacts. This includes biases introduced through data, model design, or deployment context, and covers both measurable disparities and perceived unfairness in decision-making.

Also known as: Fairness · Bias & Discrimination · Algorithmic fairness

System type: ADM and LLM systems
Lifecycle stages: Pre Processing, In Processing, Post Processing

Risk groups

• 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.
• Representational harm — Risks related to how the system represents groups in its outputs — through stereotypes, toxic content, skewed representation, or homogenization. These are harms in the form of meaning and content rather than allocation of resources or opportunities.
• Dynamic & Systemic bias — Risks related to how bias is amplified or perpetuated over time through interaction between AI systems and the social systems they operate in.

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 Outdated means it was reviewed, but the concept’s definition has changed since, so the review has to be repeated.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 Mappings to a superseded edition of a framework are not shown: where the provision continues, they are carried to the current edition.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

Taxonomies & vocabularies