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.
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. |