Responsible AI

Groundedness Testing

Every AI response must be grounded in source material. We evaluate:

• Response Accuracy: Does the response accurately reflect the source documents?
• Citation Quality: Are citations relevant and accurate?
• Hallucination Detection: Automated detection of unsupported claims
• Confidence Scoring: Confidence levels for each response component

Citation Accuracy Measurement

Citations are only valuable if they're accurate. We measure:

• Citation Relevance: Do citations support the claims made?
• Citation Completeness: Are all claims properly cited?
• Source Verification: Can citations be verified against source documents?
• Citation Precision: Are citations specific enough to be useful?

Refusal Behavior Handling

AI systems must know when to refuse requests. We evaluate:

• Appropriate Refusal: Does the system refuse out-of-scope or unsafe requests?
• Refusal Clarity: Are refusal messages clear and helpful?
• False Refusals: Does the system refuse valid requests?
• Escalation Paths: Clear paths for handling refused requests

Where HITL is Implemented

Human oversight is built into critical decision points:

• High-Stakes Decisions: Financial transactions, compliance approvals, legal interpretations
• Low-Confidence Responses: When AI confidence falls below threshold
• Novel Situations: Scenarios not seen in training data
• Policy Violations: Potential violations of policies or regulations

Approval Workflows

Structured workflows ensure human oversight:

• Multi-Stage Approval: Multiple reviewers for critical decisions
• Role-Based Approval: Approval requirements based on decision impact
• Time-Bounded Reviews: Automatic escalation if not reviewed in time
• Approval Audit Trails: Complete records of who approved what and when

Escalation Processes

Clear escalation paths for complex situations:

• Automatic Escalation: Escalate based on risk scores or confidence levels
• Manual Escalation: Users can escalate for human review
• Expert Routing: Route to domain experts based on content type
• Feedback Loops: Learn from escalations to improve future performance

Continuous Monitoring Approach

Real-time monitoring ensures systems operate correctly:

• Performance Metrics: Latency, throughput, error rates
• Quality Metrics: Accuracy, relevance, completeness
• Usage Metrics: User adoption, query patterns, feature usage
• Anomaly Detection: Automated detection of unusual patterns

Regression Testing Framework

Automated testing prevents quality degradation:

• Baseline Comparisons: Compare new versions against baseline metrics
• Automated Test Suites: Comprehensive test coverage for critical scenarios
• CI/CD Integration: Tests run automatically before deployment
• Performance Regression Detection: Alert on performance degradation

Alert Thresholds

Proactive alerting for quality issues:

• Quality Degradation: Alert when accuracy drops below threshold
• Error Rate Spikes: Alert on sudden increases in errors
• Confidence Drops: Alert when confidence levels decrease
• Usage Anomalies: Alert on unusual usage patterns

Audit Trail Capabilities

Complete audit trails for compliance and accountability:

• Query Logging: Every query logged with timestamp, user, and context
• Response Logging: Complete responses with citations and confidence scores
• Decision Logging: All AI decisions logged with reasoning
• Change Logging: All configuration and model changes logged

Version Control for Prompts & Models

Complete versioning ensures reproducibility:

• Prompt Versioning: Version control for all prompts
• Model Versioning: Track model versions and changes
• Configuration Versioning: Version control for system configurations
• Rollback Capabilities: Quick rollback to previous versions

Reproducibility Guarantees

Reproducible results for debugging and compliance:

• Deterministic Responses: Same input produces same output (where applicable)
• Reproducible Evaluations: Evaluation results can be reproduced
• Experiment Tracking: Track experiments and their results
• Documentation: Complete documentation for reproducibility

Bias Detection and Mitigation

Proactive bias detection and mitigation:

• Bias Testing: Regular testing for bias across demographic groups
• Fairness Metrics: Quantitative measures of fairness
• Mitigation Strategies: Techniques to reduce bias in outputs
• Ongoing Monitoring: Continuous monitoring for bias emergence

Privacy-Preserving Techniques

Privacy protection built into the system:

• Data Minimization: Only process necessary data
• Anonymization: Anonymize sensitive data where possible
• Access Controls: Strict access controls to protect privacy
• Data Retention: Automatic deletion of data after retention period

Fairness Evaluation

Comprehensive fairness assessment:

• Group Fairness: Equal treatment across demographic groups
• Individual Fairness: Similar individuals treated similarly
• Fairness Metrics: Quantitative fairness measures
• Fairness Reporting: Regular fairness reports for stakeholders