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.
AI/ML / Multi Agent Refarch / Capabilities / DEV
Human supervision and oversight
CCC.MARefArc.CP21
Related Threats
| ID | Title | Description |
|---|---|---|
| CCC.MARefArc.TH23 | Discriminatory outputs from bias | 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. |
| CCC.MARefArc.TH24 | Lack of explainability and traceable rationale | Black-box foundation models produce outputs without traceable rationale, leaving the firm unable to justify AI-driven decisions to regulators, stakeholders, or customers and allowing latent errors or biases to go undetected; observability and human oversight are the principal mitigating surfaces. |
| CCC.MARefArc.TH25 | Non-compliant outputs and model-risk-management gaps | AI-generated advice, marketing, or communications that fail KYC, suitability, disclosure, record-keeping, or model-risk-management expectations create regulatory exposure; weak supervision and accountability lines turn this into direct non-compliance. |