Consequential Domains
Where AI outputs trigger real-world consequences, Reality Fidelity prevents incomplete decisions.
What Makes a Domain Consequential?
A domain is consequential when AI outputs directly affect people's health, finances, freedom, safety, or life opportunities. In these domains:
- Errors cannot be easily reversed
- Stakes are high for individuals and organizations
- Regulatory oversight is present or emerging
- Trust is essential for system adoption
Healthcare
AI systems that diagnose, recommend treatments, or make clinical decisions must operate with complete patient context.
Authoritative Outputs:- Diagnostic classifications
- Treatment recommendations
- Risk stratification scores
- Triage decisions
- Complete patient history
- Current medications and allergies
- Calibrated models for patient population
- Physician oversight confirmation
Finance
Credit decisions, fraud detection, and investment recommendations affect financial wellbeing and access to opportunity.
Authoritative Outputs:- Credit approval/denial
- Fraud alerts
- Investment recommendations
- Risk scores
- Complete financial profile
- Verified identity
- Fair lending compliance check
- Model calibration for applicant segment
Legal
Contract analysis, case prediction, and compliance automation require rigorous verification before outputs.
Authoritative Outputs:- Contract risk classifications
- Case outcome predictions
- Compliance determinations
- Document review decisions
- Complete document corpus
- Jurisdiction-specific rules
- Attorney review for critical decisions
- Precedent database currency
Insurance
Claims processing, underwriting, and risk assessment directly impact coverage and financial protection.
Authoritative Outputs:- Claims approval/denial
- Premium calculations
- Risk classifications
- Fraud determinations
- Complete application data
- Actuarial model validation
- Regulatory compliance verification
- Anti-discrimination checks
Child Safety
Content moderation, risk assessment, and intervention systems protect vulnerable populations.
Authoritative Outputs:- Content classifications
- Risk assessments
- Intervention recommendations
- Escalation decisions
- Complete context (not just content)
- Age verification where applicable
- Human review for edge cases
- Cultural and linguistic context
Employment
Hiring, performance evaluation, and workforce decisions affect livelihoods and careers.
Authoritative Outputs:- Candidate rankings
- Interview recommendations
- Performance scores
- Termination risk flags
- Complete candidate profile
- Job requirement validation
- Bias audit completion
- Human oversight confirmation
Government & Benefits
Benefits eligibility, public assistance, and administrative decisions affect access to essential services.
Authoritative Outputs:- Eligibility determinations
- Benefit calculations
- Fraud risk scores
- Case prioritization
- Household composition verification
- Income documentation
- Jurisdictional rule sets
- Appeal rights notification
Engineering & Manufacturing
Design specifications, safety calculations, and quality control affect product integrity and human safety.
Authoritative Outputs:- Material specifications
- Safety factor calculations
- Quality classifications
- Compliance certifications
- Load and stress requirements
- Environmental exposure conditions
- Standards compliance verification
- Engineering review sign-off
Education & Learning
Student assessment, curriculum recommendations, and learning path decisions affect educational outcomes.
Authoritative Outputs:- Proficiency assessments
- Curriculum recommendations
- Intervention triggers
- Placement decisions
- Learning objective frameworks
- Prior assessment history
- Accommodation requirements
- Educator review confirmation
Cross-Domain Patterns
Despite domain-specific requirements, Reality Fidelity reveals common patterns across consequential domains:
- Incomplete Input Problem — Systems operating without all required information
- Calibration Drift — Models applied outside their validated context
- Oversight Gaps — Human review requirements bypassed or ignored
- Provenance Opacity — Inability to trace outputs back to source data