AAT-22: AI & Autonomous Technologies Fairness & Bias
Mechanisms exist to prevent Artificial Intelligence (AI) and Autonomous Technologies (AAT) from unfairly identifying, profiling and/or statistically singling out a segmented population defined by race, religion, gender identity, national origin, disability or any other politically-charged identifier.
Renumbered: Formerly AAT-06 in SCF 2025.4.
Code reused: In SCF 2025.4 and earlier, AAT-22 was AI & Autonomous Technologies Deployment. That control is now AAT-35.
Control Question: Does the organization prevent Artificial Intelligence (AI) and Autonomous Technologies (AAT) from unfairly identifying, profiling and/or statistically singling out a segmented population defined by race, religion, gender identity, national origin, religion, disability or any other politically-charged identifier?
General (3)
| Framework | Mapping Values |
|---|---|
| NIST AI 100-1 (AI RMF) 1.0 | GOVERN 3.0 |
| NIST AI 600-1 | MS-2.2-001 MS-2.11-001 MS-3.3-003 |
| Shared Assessments SIG 2025 | R.4.1 |
US (1)
| Framework | Mapping Values |
|---|---|
| USA Texas TRAIGA 2025 | 552.056(b) |
Capability Maturity Model
Level 0 — Not Performed
Practices are non-existent, based on the inability to demonstrate an implemented and operational capability. A reasonable person would conclude the control is not being performed.
Level 1 — Performed Informally
Artificial Intelligence and Autonomous Technology (AAT) domain capabilities are ad hoc and inconsistent. Capability criteria associated with this control may include: ▪ Policies, standards & procedures associated with AAT domain capabilities provide limited coverage due to the depth and breadth of the existing documentation. ▪ AAT-related processes are expected to follow the organization's existing processes (e.g., incident response, asset management, change control and risk assessments). ▪ No formal Governance, Risk & Compliance (GRC) team exists to provide AAT oversight, where the Chief Information Officer (CIO), or similar function, governs technology decisions what is acceptable for AAT within the organization.
Level 2 — Planned & Tracked
Artificial Intelligence and Autonomous Technology (AAT) capabilities are requirements-driven, but are not standardized across the entity (e.g., local/regional level consistency). Capability criteria associated with this control reasonably expect the following criteria to exist: ▪ Policies and standards associated with AAT domain capabilities are formally documented and centrally-managed by the entity. ▪ Standardized Operating Procedures (SOP) associated with AAT domain capabilities are documented and maintained by process owners. ▪ IT and/or cybersecurity personnel work with business stakeholders and process owners to appropriately scope and reasonably implement cybersecurity and data protection controls associated with AAT domain capabilities to address applicable statutory, regulatory and/or contractual requirements for Technology Assets, Applications, Services and/or Data (TAASD). ▪ Artificial Intelligence (AI)-related controls are primarily administrative and preventative in nature (e.g., policies, standards and procedures & guidelines). ▪ Asset management may be a defined function (e.g., team or department) or assigned as an additional duty to existing IT and/or cybersecurity personnel.
Level 3 — Well Defined
Artificial Intelligence and Autonomous Technology (AAT) capabilities are standardized across the entity for applicability to People, Processes, Technologies, Data and/or Facilities (PPTDF) to ensure consistency for Technology Assets, Applications, Services and/or Data (TAASD). Capability criteria associated with this control reasonably expect the following criteria to exist: ▪ Policies and standards associated with AAT domain capabilities are formally documented and centrally-managed by the entity's Governance, Risk & Compliance (GRC) team, or similar function. ▪ Standardized Operating Procedures (SOP) associated with AAT domain capabilities are well-documented and kept current by process owners. ▪ An Artificial Intelligence Governance (AIG) team, or similar function, is appropriately staffed and supported to implement and maintain AAT domain capabilities. ▪ Technology is leveraged to enhance the efficiency and accuracy of AI governance, risk management and compliance operations (e.g., dedicated AI governance platform). ▪ The entity's Governance, Risk & Compliance (GRC) team, or similar function, works with business stakeholders and process owners to appropriately scope and reasonably implement cybersecurity and data protection controls associated with AAT domain capabilities to address Minimum Compliance Requirements (MCR) (e.g., applicable statutory, regulatory and/or contractual requirements) and Discretionary Security Requirements (DSR) (e.g., entity-required controls). ▪ An implemented and operational capability exists to prevent AAT from unfairly identifying, profiling and/or statistically singling out a segmented population defined by race, religion, gender identity, national origin, religion, disability or any other politically-charged identifier.
Level 4 — Quantitatively Controlled
Utilize SCR-CMM Level 3 criteria definitions: ▪ There are no defined Level 4 criteria, since it is reasonable to assume a quantitatively-controlled process is not necessary to operationalize this control. ▪ While it may be possible to develop “metrics-driven” capabilities for this control, the criteria would be organization-specific to define.
Level 5 — Continuously Improving
Utilize SCR-CMM Level 3 or Level 4 (if available) criteria definitions: ▪ There are no defined Level 5 criteria, since it is reasonable to assume a continuously-improving process is not necessary to operationalize this control. ▪ Level 5 capabilities should be considered “world-class” where the control builds on Level 4 capabilities, but are continuously improving through Artificial Intelligence (AI) and/or Machine Learning (ML) technologies. ▪ While it may be possible to develop responsive capabilities for this control through the use of AI and/or ML technologies, the criteria would be organization-specific to define.
Assessment Objectives
- AAT-22_A01 a documented methodology prioritizes workforce diversity, equity, inclusion and accessibility processes in the mapping, measuring and managing of Artificial Intelligence (AI) and Autonomous Technologies (AAT)-related risks throughout the AAT lifecycle.
- AAT-22_A02 Artificial Intelligence (AI) and Autonomous Technologies (AAT) are tested for outputs that unfairly identify, profile and/or statistically single out a segmented population defined by race, religion, gender identity, national origin, disability or any other politically-charged identifier, and identified issues are remediated.
Evidence Requirements
- E-AAT-06 Impact Assessments for Artificial Intelligence and Autonomous Technologies (AAT)
-
Documented evidence of impact assessments of proposed Artificial Intelligence (AI) and Autonomous Technologies (AAT) on individuals, groups, communities, organizations and society.
Artificial Intelligence (AI) & Autonomous Technologies Governance
Technology Recommendations
Micro/Small
- Basic AI bias review before deploying AI tools (vendor documentation review)
- AI governance program
- NIST AI RMF fairness resources
Small
- AI bias and fairness assessment for AI systems in use
- Vendor AI fairness and non-discrimination documentation review
- AI governance program
Medium
- Formal AI fairness and bias testing process
- Diverse training data requirements in AI procurement
- NIST AI RMF fairness and bias guidance implementation
- AI governance program
Large
- AI fairness testing integrated into AI development and procurement lifecycle
- Statistical bias testing tools (e.g., IBM AI Fairness 360, What-If Tool, Fairlearn)
- Documented AI fairness policies and standards
- Independent fairness review for high-risk AI applications
Enterprise
- Enterprise AI fairness governance program
- Automated bias detection in AI pipelines (e.g., IBM AI Fairness 360, Fairlearn, AWS Clarify)
- AI fairness audits by independent third parties
- AI disparate impact analysis tied to regulatory compliance (ECOA, FCRA, etc.)