INITE Responsible AI Framework
Complete governance, transparency, and ethical AI principles aligned with EU AI Act and NIST AI Risk Management Framework. Published Q1 2025.
Executive Summary
INITE Solutions is committed to developing and deploying AI systems that are safe, transparent, fair, and accountable. This framework outlines our policies, procedures, and technical controls for responsible AI development and deployment.
100%
Models with Risk Assessment
Quarterly
Bias Audits
24h
Incident Response SLA
SHAP/LIME
Explainability Available
EU AI Act Compliance
Our AI systems are designed to comply with the European Union's Artificial Intelligence Act (EU AI Act), the world's first comprehensive AI regulation.
Risk Classification
- ✓All AI systems categorized by risk level (minimal, limited, high)
- ✓Customer service AI classified as limited/minimal risk
- ✓No prohibited AI practices (social scoring, manipulation)
- ✓Human oversight for all automated decisions
Transparency Obligations
- ✓Clear disclosure when users interact with AI
- ✓AI-generated content clearly labeled
- ✓Technical documentation maintained
- ✓Model Cards published for all production systems
NIST AI Risk Management Framework
We implement the NIST AI Risk Management Framework (AI RMF 1.0) to ensure trustworthy AI across all dimensions.
GOVERN
- • AI governance policies
- • Roles & responsibilities
- • Risk tolerance defined
- • Continuous monitoring
MAP
- • Context identification
- • Stakeholder analysis
- • Impact assessment
- • Use case documentation
MEASURE
- • Performance metrics
- • Fairness testing
- • Accuracy validation
- • Robustness testing
MANAGE
- • Risk mitigation plans
- • Incident response
- • Model updates
- • Continuous improvement
Bias Testing & Fairness Audits
Testing Methodology
- ●Demographic Parity: Equal treatment across protected groups (gender, age, ethnicity, language)
- ●Equal Opportunity: Equal true positive rates across groups
- ●Calibration: Predictions reflect true probabilities across groups
- ●Language Fairness: Equal quality across all 16 supported languages
Audit Schedule
- ◆Pre-deployment: Full bias audit before any model goes live
- ◆Quarterly: Regular fairness audits on production systems
- ◆Continuous: Real-time monitoring for drift and anomalies
- ◆On-demand: Client-requested audits available
Model Explainability
We provide multiple levels of explainability for AI decisions, enabling transparency and trust.
SHAP Analysis
SHapley Additive exPlanations for feature importance. Understand which factors contributed to each AI decision.
LIME Explanations
Local Interpretable Model-agnostic Explanations for individual predictions. Human-readable reasoning.
Attention Visualization
For NLP models: see which parts of input text the model focused on to generate responses.
Data Governance & Privacy
GDPR Compliance
- ✓ Data minimization principle
- ✓ Purpose limitation
- ✓ Right to access and deletion
- ✓ Right to explanation of automated decisions
- ✓ Data Processing Agreements with all clients
- ✓ Cross-border transfer safeguards (SCCs)
Security Controls
- ✓ AES-256 encryption at rest
- ✓ TLS 1.3 encryption in transit
- ✓ Role-based access control (RBAC)
- ✓ Multi-factor authentication (MFA)
- ✓ Quarterly penetration testing
- ✓ SOC 2 Type II certified infrastructure
Human Oversight & Control
All INITE AI systems include configurable human-in-the-loop controls for sensitive decisions and escalations.
Escalation Triggers
- • Low confidence predictions
- • Sensitive topics detected
- • User requests human
- • Policy violations
Override Controls
- • Human can override any AI decision
- • Audit trail maintained
- • Feedback loop for model improvement
- • Emergency shutdown capability
Monitoring
- • Real-time performance dashboards
- • Anomaly detection alerts
- • Quality assurance sampling
- • User satisfaction tracking
Incident Response
15 min
Initial Response
Critical incidents
1 hour
Escalation
To senior engineering
4 hours
Mitigation
Initial fix deployed
24 hours
Post-mortem
Root cause analysis
Quarterly Transparency Report
We publish quarterly transparency reports covering AI system performance, incidents, and improvements.
Q4 2024 Highlights
- • 99.8% uptime across all production systems
- • 0 critical security incidents
- • 2 bias audits completed (all passed)
- • 3 model updates with improved fairness
- • Average response time: 2.3 seconds
Report Contents
- • System performance metrics
- • Incident summary and resolutions
- • Bias audit results
- • User feedback analysis
- • Upcoming improvements
Related Documentation
Questions About AI Governance?
Contact us for detailed compliance documentation, audit reports, or custom governance requirements.
📧 Email: [email protected]