Building Trust in AI Systems
Through Governance
Hertz Matrix provides a comprehensive trust and governance framework - from responsible AI principles to the AI Trust Score™, human oversight, explainability, fairness, and security.
Foundations of Trustworthy AI
Guiding principles that underpin the Hertz Matrix trust and governance framework.
Human-Centric
AI systems are designed to enhance human capabilities and well-being.
Transparent
AI decisions are explainable and understandable to stakeholders.
Accountable
Clear accountability for AI outcomes and impacts.
Fair and Inclusive
AI systems are designed to be fair and avoid bias.
Secure
Robust security and privacy protections for AI systems and data.
Reliable
AI systems perform consistently and reliably across scenarios.
Measuring Trust in AI Systems
A structured methodology for assessing AI systems across multiple trust dimensions.
Accuracy
Assessment of AI system performance accuracy and reliability.
Fairness
Evaluation of bias and fairness across different groups.
Security
Assessment of security controls and vulnerability management.
Compliance
Mapping to regulatory and compliance requirements.
Evidence
Quality and completeness of audit evidence.
Oversight
Human oversight and governance controls.
The AI Trust Score™ methodology is currently under design and validation. Published scores shown on this website are illustrative only.
Human-Centered AI Governance
Ensuring meaningful human oversight of AI systems at all levels.
Human-in-the-Loop
- Critical decisions require human review
- Human oversight of AI outputs
- Exception handling workflows
- Escalation procedures
Oversight Controls
- Governance committees
- AI oversight boards
- Review workflows
- Decision accountability
Continuous Monitoring
- Real-time performance monitoring
- Alert and notification systems
- Incident response
- Regular review cycles
Understanding AI Decisions
Making AI decisions transparent and understandable to stakeholders.
Model Explainability
Techniques to explain model decisions and predictions
Decision Documentation
Comprehensive documentation of AI decision processes
Transparency Reports
Regular transparency and explainability reporting
Ensuring Fair AI Outcomes
Systematic approaches to identify and mitigate bias in AI systems.
Bias Detection
- Algorithmic bias assessment
- Training data analysis
- Performance metrics by group
- Regular bias audits
Fairness Metrics
- Statistical fairness measures
- Equal opportunity assessment
- Group fairness evaluation
- Bias mitigation tracking
Mitigation Strategies
- Bias mitigation techniques
- Model retraining
- Data augmentation
- Ongoing monitoring
Secure AI Systems
Robust security controls for AI systems and data protection.
Data Protection
Encryption and data security controls
Threat Protection
Vulnerability management and threat detection
Access Control
Identity and access management
Security Monitoring
Continuous security monitoring and alerts
Comprehensive Evidence
Maintaining robust evidence for AI governance and compliance.
Audit Evidence
- Decision records
- Model documentation
- Review evidence
- Approval records
Evidence Repository
- Centralized storage
- Evidence catalog
- Version control
- Search and retrieval
Certification
- Compliance certification
- Audit reports
- Performance validation
- Security attestation
Clear Accountability
Defining and maintaining clear accountability for AI systems and outcomes.
Role Definition
- AI owners and sponsors
- Governance roles
- Review responsibilities
- Accountability chains
Governance Framework
- Accountability matrix
- Decision authority
- Escalation paths
- Reporting structure
Documentation
- Accountability records
- Decision records
- Review documentation
- Compliance reports
Comprehensive Policy Framework
Defining and enforcing AI governance policies and controls.
Policy Development
- Acceptable use policies
- Data governance policies
- Model governance policies
- Ethical AI guidelines
Control Implementation
- Control mapping
- Implementation tracking
- Effectiveness monitoring
- Control remediation
Continuous Enforcement
- Automated policy enforcement
- Compliance monitoring
- Exception handling
- Policy updates
Your Path to AI Compliance
A structured approach to AI compliance and regulatory readiness.
Assessment
- Current state analysis
- Gap assessment
- Regulatory mapping
- Priority identification
Implementation
- Framework design
- Policy development
- Control implementation
- Process deployment
Validation
- Testing and validation
- Audit preparation
- Compliance verification
- Certification readiness
Continuous Monitoring
- Ongoing compliance
- Regular audits
- Policy updates
- Continuous improvement