risk
Harmful AI bias and discrimination against protected groups
Models encode/amplify historical bias, producing allocative harm (biased hiring/lending/housing/benefits), representational harm (stereotyping), and evaluation bias masking disparate subgroup performance — systematically disadvantaging protected groups.
In catalog since 2026-09-17T22:28:00Z · Last changed 2026-09-17T22:28:00Z (f368a6cce277)
Record JSON · Open in map · Data retrieval guide
Catalog revision: 791ff2dd3a45707290badee660f185e514d15f1cf518908628f425c2f2c56ee4. A connection does not establish full coverage.
Attributes
- category
- ai_governance
- domain
- AI Governance
- Data Protection & Privacy
- Compliance, Audit & Assurance
- taxonomy
- nist-ai-rmf-risk
- iso-23894-ai-risk
- nist-privacy-risk
- inherent_rating
- high
Details
- risk_id
- ai-bias-discrimination
- category
- ai_governance
- likelihood
- high
- impact
- high
- inherent_rating
- high
- treatment
- mitigate
- taxonomies
- nist-ai-rmf-risk
- iso-23894-ai-risk
- nist-privacy-risk
Source
No record-specific source URL is provided.
Connections
- UC-AI-09 — Govern AI data quality, provenance, and preparation mitigates Harmful AI bias and discrimination against protected groups
- strength
- primary
- rationale
- Examining datasets for biases and mitigating those affecting rights is the data-source control against discriminatory model behavior.
- UC-AI-07 — Verify, validate, and control AI deployment and changes mitigates Harmful AI bias and discrimination against protected groups
- strength
- related
- rationale
- Validating against responsible-AI fairness objectives tests for discriminatory behavior before release.
- UC-AI-20 — Prevent harmful, out-of-scope, hallucinated, and over-exposed AI outputs mitigates Harmful AI bias and discrimination against protected groups
- strength
- related
- rationale
- Harmful-output categories typically include discriminatory content, so output filtering catches some biased responses.
- UC-AI-05 — Set responsible AI development objectives and requirements mitigates Harmful AI bias and discrimination against protected groups
- strength
- related
- rationale
- Embedding fairness as a design objective drives bias prevention upstream, later verified/remediated by UC-07/UC-09.
- UC-AI-11 — Operate AI concern, incident, and external reporting channels mitigates Harmful AI bias and discrimination against protected groups
- strength
- related
- rationale
- Stakeholder concern channels surface discrimination complaints for triage and remediation.