Biased training data, architectural and feature choices, proxy variables such as postal codes, and uncorrected feedback loops cause systematically discriminatory outcomes against protected groups, with legal and reputational exposure.
AI/ML / Multi Agent Refarch / Threats / DEV
Discriminatory outputs from bias
CCC.MARefArc.TH23
Related Capabilities
| ID | Title | Description |
|---|---|---|
| CCC.MARefArc.CP14 | Approved-model registry and lifecycle | Catalog of approved models with metadata, version information, configuration parameters, and usage constraints, ensuring agents access only models meeting organizational, regulatory, and security standards. |
| CCC.MARefArc.CP12 | Authoritative knowledge source bases | Internal and external repositories of structured data, unstructured documents, and graph-based representations that provide authoritative information for grounding. |
| CCC.MARefArc.CP20 | Feedback engine | Collects and aggregates structured and unstructured feedback from users, evaluators, and automated systems, including correctness assessments, preference signals, and quality ratings, to inform system improvement. |
| CCC.MARefArc.CP21 | Human supervision and oversight | Mechanisms for human reviewers to inspect, approve, correct, or override agent outputs, supporting human-in-the-loop and human-over-the-loop workflows for sensitive or high-impact tasks. |
Related Controls
| ID | Title | Description |
|---|---|---|
| CCC.MARefArc.CN03 | System Acceptance Testing | Validate agents, models, and end-to-end workflows against accuracy, robustness, bias, drift, and compliance criteria before promotion to production, and re-validate after material changes. |
| CCC.MARefArc.CN04 | Data Quality and Classification | Assess the quality of, and assign classification and sensitivity labels to, all data used for grounding, training, and fine-tuning, and enforce handling rules derived from those labels throughout the Knowledge and LLM layers. |
| CCC.MARefArc.CN19 | Human Feedback Loop for AI Systems | Capture human feedback on agent outputs through the Feedback Engine and Human Supervision capabilities and feed it into evaluation and improvement of agents and models. |
| CCC.MARefArc.CN21 | Automated Evaluation Using LLM-as-a-Judge | Use automated model-based evaluation in the Evaluation Layer to assess output quality, grounding, bias, and policy compliance at scale. |
External Mappings
| Framework | ID | Remarks |
|---|---|---|
| air-vec | AIR-OP-016-01 | |
| air-vec | AIR-OP-016-02 | |
| air-vec | AIR-OP-016-03 | |
| air-vec | AIR-OP-016-04 |