Go/No-Go Decision

A Go/No-Go gate is not an informal checkpoint but a scheduled, mandatory meeting with a predefined agenda and required attendees. The decision is binary: proceed (Go) or halt (No-Go). A conditional or deferred decision is treated as a No-Go until conditions are met. The outcome is formally documented with a Decision Log Entry capturing the rationale, dissenters, and any accepted residual risk.

The gate is governed by a set of quantitative and qualitative criteria established at the project's outset. These are not subjective opinions but measurable thresholds:

• Performance Metrics: Model accuracy, F1 score, or latency must exceed a specific baseline.
• Risk Assessment: All identified risks from the Algorithmic Impact Assessment must be below a defined severity level.
• Compliance Checks: Verification of alignment with EU AI Act requirements for the designated risk category.
• Documentation Completeness: Model Cards and Transparency Notices must be finalized and reviewed.

A specific individual or body holds the authority to declare a Go decision. This is typically a Human Accountability Anchor or a Change Advisory Board (CAB) for high-risk systems. This authority cannot be delegated to an automated system. The approver must be independent of the development team to avoid conflicts of interest and must sign a Risk Acceptance Sign-off if any criteria are waived or residual risks remain above the threshold.

Every aspect of the gate process must be recorded in an immutable, timestamped log for regulatory audit. This includes:

• The complete evidence package presented (test reports, bias scans, adversarial robustness evaluations).
• The attendance roster and any declared conflicts of interest.
• The final decision, the vote tally if applicable, and the written justification.
• Any Deviation Authorizations or overrides of standard policy. This log serves as the legal record of due diligence for the AI Audit Trail Immutability requirement.

A gate must have a predefined Escalation Protocol for deadlocks. If the designated authority cannot reach a consensus or a critical risk is disputed, the decision is automatically escalated to a higher governance body. The default state of any gate is a No-Go. If the review is not completed by the scheduled date, the system does not proceed by default. This aligns with the Fallback Protocol principle, ensuring safety through inaction.

A Go decision is often granted with a binding condition for intensified post-market monitoring. The gate approval may specify a probationary period where specific metrics (e.g., fairness indicators, error rates) are tracked in real-time. A Guardrail Violation Flag during this period can automatically trigger a re-review or a temporary suspension, effectively reverting the decision to a No-Go state without requiring a new full gate meeting.

A system design where a human operator is a required component of the decision-making process. In the context of a Go/No-Go gate, HITL is the active mechanism executing the decision.

• Active Approval: The human provides explicit judgment before an AI output is finalized.
• Go/No-Go Integration: The HITL operator is the final arbiter at the gate, reviewing test results and risk assessments.
• Contrast with HOTL: Unlike passive monitoring, HITL demands a deliberate action to proceed.

A routing mechanism that automatically escalates a decision to a human review queue when the AI model's prediction confidence score falls below a predefined boundary. This is a primary technical trigger for a Go/No-Go review.

• Automated Triage: Low-confidence outputs are automatically flagged for human authorization.
• Threshold Calibration: The boundary is domain-specific; a medical diagnosis tool has a far higher threshold than a content recommender.
• Workflow: The system does not proceed autonomously; it awaits a human Go/No-Go signal.

A formal acknowledgment by a designated authority that they understand and accept the residual risk of deploying an AI system without fully mitigating a known vulnerability. This is the legal and operational output of a Go/No-Go decision.

• Accountability Anchor: The signatory becomes the Human Accountability Anchor for that specific risk.
• Documentation: Creates an auditable record that a conscious decision was made to proceed despite known issues.
• Precondition: Often requires a completed Algorithmic Impact Assessment before the sign-off can be executed.

A cognitive bias where a human operator over-relies on an AI system's recommendation, ignoring contradictory information. This is a critical threat to the integrity of a Go/No-Go decision.

• Decision Contamination: The human reviewer may rubber-stamp an AI's 'Go' recommendation without sufficient scrutiny.
• Mitigation: Structured checklists and the Four-Eyes Principle are used to counteract this bias during the review.
• Training: Operators must be trained to actively seek disconfirming evidence, not just validate the model's output.

A structured, hierarchical procedure that defines how an AI-generated issue or anomaly is progressively routed to higher levels of human authority. A Go/No-Go decision is the final step in a critical escalation path.

• Severity-Based Routing: A model drift anomaly might escalate to a lead engineer, while a potential bias violation escalates directly to a compliance officer for a No-Go decision.
• Time Sensitivity: Defines maximum response times for each escalation level to prevent decision paralysis.
• Integration: The protocol defines who has the authority to make the final Go/No-Go call at each tier.

A security and compliance control requiring that a critical action, such as deploying a model or approving a high-risk decision, is authorized by at least two separate human operators. This is a structural safeguard for high-stakes Go/No-Go gates.

• Dual Authorization: Prevents a single point of human failure or malfeasance.
• Segregation of Duties: Ideally, the two approvers come from different functions—e.g., a technical lead and a compliance officer.
• Implementation: Enforced technically in the deployment pipeline, not just as a policy.