Out-of-Scope Use Cases

Clearly enumerates entire verticals or fields where the model must not be applied. This goes beyond vague warnings to name specific industries, tasks, or decision types.

• High-Risk Domains: Explicitly prohibit use in medical diagnosis, legal sentencing, or critical infrastructure control.
• Regulated Verticals: Name specific frameworks like HIPAA-covered activities or FCRA-governed credit decisions.
• Safety-Critical Systems: Disallow integration into emergency response, autonomous vehicle control, or life-support systems.

This transforms a general disclaimer into a testable, auditable boundary for compliance teams.

Documents the known functional limitations of the model based on its architecture, training data, and evaluation results. This is a statement of technical honesty, not legal hedging.

• Input Modality Constraints: Specify unsupported data types (e.g., 'not validated on multi-channel audio' or 'does not process DICOM images').
• Language and Locale Gaps: List languages, dialects, or cultural contexts where performance is unverified or known to degrade.
• Temporal Drift Sensitivity: State the training data cutoff date and warn against use on post-cutoff events or rapidly evolving domains.

These boundaries are derived directly from the model card and benchmark dataset evaluations.

Identifies demographic or user segments for whom the model's outputs are unreliable, harmful, or legally impermissible. This operationalizes fairness metrics into deployment policy.

• Age-Gating: Prohibit use for users under a specified age, particularly in educational or mental health contexts.
• Vulnerable Groups: Disallow application to populations under duress, such as asylum seekers or crisis hotline users.
• Protected Class Profiling: Explicitly forbid use cases that could reconstruct or infer sensitive attributes like race, religion, or sexual orientation.

This section directly addresses disparate impact ratio risks and stakeholder impact assessment findings.

Defines the environmental and deployment conditions under which the model is not validated. This prevents silent failures when the system is dropped into an untested context.

• Automation Level: Specify if the model is restricted to human-in-the-loop or human-on-the-loop modes only, prohibiting fully autonomous decision-making.
• Throughput and Latency: State maximum query volumes or latency requirements beyond which behavior is undefined.
• Adversarial Environments: Disallow deployment in contexts where users are incentivized to actively game the model via prompt injection or evasion attacks.

These restrictions are critical for continuous compliance monitoring and AI incident response planning.

Anticipates and forbids specific composite use cases where the model could be a component in a larger harmful system. This addresses system card-level risks.

• Surveillance Chain: Prohibit use as a component in facial recognition, sentiment analysis for employee monitoring, or social scoring pipelines.
• Disinformation Amplification: Disallow integration into systems designed to generate deceptive content, impersonate individuals, or manipulate public opinion.
• Weapon Systems: Explicitly forbid use in target acquisition, autonomous weapons, or munitions guidance, aligning with algorithmic registry ethical standards.

This requires thinking beyond the model's direct output to its role in a multi-agent system orchestration or broader software ecosystem.

Links the out-of-scope specification to a specific, immutable model version. When a model is updated, the specification must be re-evaluated and re-issued.

• Immutable Binding: The specification is cryptographically tied to a specific model versioning hash or SBOM.
• Sunset Clause: States that the specification is void if the model is fine-tuned, distilled, or otherwise modified without a new model provenance audit.
• Supersession Logic: Defines the process by which a new specification replaces an old one, ensuring no deployment gap exists.

This practice is fundamental to model lineage integrity and AI audit trail immutability.

A precise declaration defining the specific purpose, target domain, and operational constraints for which an AI system was designed and validated. This statement serves as the positive boundary that Out-of-Scope Use Cases negate.

• Defines the validated operational envelope
• Specifies target users and deployment context
• Forms the legal basis for conformity assessments under the EU AI Act

Without a clear Intended Use Statement, Out-of-Scope Use Cases cannot be meaningfully enumerated.

A structured transparency document detailing a model's intended use, performance metrics, evaluation data, and known limitations. The Out-of-Scope Use Cases section is a mandatory field in standardized model card frameworks.

• Originated from Google's 2019 research paper
• Standardized by Hugging Face and the MLCommons consortium
• Includes ethical considerations and caveats

Model cards transform Out-of-Scope declarations from internal policy into public-facing transparency artifacts.

A holistic transparency artifact that documents the safety evaluation and operational context of an entire AI system, including its model, user interface, and downstream effects. Unlike a model card, a system card captures socio-technical failure modes.

• Documents human-AI interaction risks
• Includes red-teaming results and misuse simulations
• Required by frontier model developers under voluntary White House commitments

System cards contextualize Out-of-Scope Use Cases within the broader deployment ecosystem.

A systematic process for identifying and evaluating the potential positive and negative effects of an AI system on all affected parties before deployment. Out-of-Scope Use Cases are a critical input to this assessment.

• Mandated by Canada's Directive on Automated Decision-Making
• Evaluates fundamental rights impact under the EU AI Act
• Requires stakeholder consultation and mitigation planning

The assessment uses Out-of-Scope declarations to identify foreseeable misuse pathways that could cause harm.

The design principle ensuring that individuals can effectively challenge, seek remedy for, or correct an automated decision. When a model is used outside its defined scope, contestability mechanisms provide recourse.

• Requires accessible appeal channels
• Links to the GDPR's right to explanation
• Depends on clear scope boundaries for validity

If a decision was made using a model in an Out-of-Scope context, contestability frameworks provide the legal basis for invalidation.

A regulatory remedy requiring a company to delete a trained model or its associated data products when they were developed using unlawfully collected or improperly processed personal data. This is the ultimate enforcement mechanism for scope violations.

• Ordered by the FTC in multiple enforcement actions
• Applies when models are trained on data beyond consented purposes
• Requires full model lineage tracking to execute

Out-of-Scope Use Cases that violate purpose limitation can trigger disgorgement orders.