Industries

Independent evidence where AI decisions carry real consequences.

Clause5afe’s certification model is designed for AI systems whose outputs can affect health, opportunity, financial security, professional rights, organizational resilience, or public safety.

Regulatory classification depends on the system’s function, use, users, jurisdiction, and deployment context. An industry label alone does not determine certification scope.

The shared question

What could happen to a person when this system acts?

Different industries use different systems, evidence, and controls. The common starting point is the consequence: who can be affected, what rights or safety interests are involved, and whether the system’s claims can withstand independent examination.

01Clinical and patient impact

Healthcare AI

AI can influence diagnosis, treatment, triage, patient matching, records, and access to care. The consequences of an incorrect or poorly governed result can reach a person immediately.

Common use cases

Clinical decision support
Diagnostic imaging
Patient triage and matching
Behavioral-health analytics
Drug and treatment-pathway systems
AI embedded in health records

Typical evidence focus

Human oversight
Data and consent
Performance boundaries
Material-change control

02Access, eligibility, and financial consequence

Financial Services & Fintech

Automated systems can influence whether people receive credit, how risk is priced, which transactions are stopped, and how financial institutions act on detected behavior.

Common use cases

Credit decisioning
Fraud detection
Anti-money-laundering systems
Risk assessment
Consumer lending automation
Algorithmic market systems

Typical evidence focus

Fairness and consistency
Explainability
Data quality
Appeal and human review

03Operational and infrastructure protection

Cybersecurity

Threat-detection and automated-response systems make rapid decisions about access, containment, identity, and risk. Their failures can expose entire organizations and connected communities.

Common use cases

Email threat detection
Automated incident response
Behavioral analytics
Vulnerability prioritization
Phishing defense
Identity and access systems

Typical evidence focus

Adversarial resilience
False-positive harm
Escalation controls
Incident evidence

04Rights, representation, and access to justice

Legal Technology

Legal AI can influence contracts, discovery, research, compliance, and strategic decisions. Reliability and disclosure matter when automated analysis enters a professional duty or legal proceeding.

Common use cases

Contract analysis
Legal research
Document review
E-discovery automation
Compliance monitoring
Case and matter analytics

Typical evidence focus

Source traceability
Professional oversight
Confidentiality
Known limitations

05Coverage, pricing, and claims outcomes

Insurance & Risk

AI systems can influence underwriting, pricing, fraud review, claims handling, and risk models. Those decisions can affect whether a person or organization receives protection when it matters most.

Common use cases

Underwriting systems
Claims automation
Actuarial modeling
Risk scoring
Fraud detection
Policy-pricing algorithms

Typical evidence focus

Decision consistency
Protected attributes
Human escalation
Outcome monitoring

06Livelihood and workplace consequence

HR & Employment AI

Hiring, screening, assessment, monitoring, and compensation systems can shape a person’s opportunity and working life. Independent evidence matters when automated judgments influence livelihoods.

Common use cases

Resume screening
Hiring and matching algorithms
Workforce analytics
Performance assessment
Employee monitoring
Compensation modeling

Typical evidence focus

Bias and disparate impact
Notice and consent
Human review
Challenge mechanisms

Across every sector

The audit must follow the real system, not a generic checklist.

01
What exact system, version, and deployment context is being evaluated?
02
Which people can be affected, and what happens when the system is wrong?
03
What evidence supports the system’s claimed capabilities and limits?
04
Where is human oversight meaningful rather than merely documented?
05
How are consent, privacy, fairness, security, and accountability handled together?
06
What changes would make the original certification scope stale?

Beyond the listed sectors

Consequential AI does not stop at six industries.

Robotics, education, public services, infrastructure, consumer platforms, and other deployments may also require independent certification when an AI system makes or materially influences consequential decisions.

Discuss a System See How Certification Works