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Engineering Process
Design Phase
- Define automation purpose and scope
- Document expected behavior under normal and edge cases
- Identify where human judgment is required
- Plan approval workflows and escalation paths
Review Phase
- Workflow undergoes responsible AI review before deployment
- Checklist: Is it explainable? Accountable? Fair? Secure?
- Identify any potential biases in decision criteria
- Confirm compliance requirements are met
- Get security sign-off for credential usage
Deployment Phase
- Workflows are deployed with audit logging enabled
- Initial execution in monitoring mode (no actions taken)
- Validate outputs are as expected
- Gradually increase automation scope if results are good
Monitoring Phase
- Execution metrics tracked: volume, success rate, approval rate
- Audit logs continuously reviewed
- Human override rates monitored—high rates indicate issues
- Compliance checks validate policies are enforced
- Performance alerts detect anomalies
Improvement Phase
- Regular reviews of automation effectiveness
- Feedback from users integrated into improvements
- Bias analysis: Are outcomes consistent across use cases?
- Workflow refinements based on lessons learned
Measuring Responsible AI
We track these metrics to ensure responsible AI practices are maintained:
| Metric | What it measures | Target |
|---|---|---|
| Audit Trail Completeness | % of automation actions with full audit logs | 100% |
| Human Override Rate | % of AI recommendations overridden by humans | Track trends; investigate spikes |
| Approval Latency | Average time for approval of automation actions | < 15 min for routine approvals |
| Compliance Check Coverage | % of applicable resources checked for compliance violations | 100% |
| Fairness (Consistency) | % of identical violations handled identically | > 95% |
| Security Incidents | Unauthorized actions or data breaches | 0 |
| Mean Time to Investigate | Average time to explain why automation took an action | < 5 min |
| Explainability Score | % of workflows with documented decision logic | 100% |
Continuous Improvement
Feedback Loops
- Customer feedback is captured when automations succeed or fail
- Override patterns are analyzed to improve recommendations
- Edge cases that cause failures are documented
- Improvements are incorporated into templates and training
Model Updates
- AI models used in the platform are regularly retrained
- New data patterns are incorporated
- Responsible AI principles are maintained through updates
- Changes are tested for bias before deployment
Process Reviews
- Quarterly reviews of responsible AI practices
- Post-incident reviews capture lessons learned
- Customer use cases are analyzed for best practices
- Process improvements are documented and communicated
Stakeholder Engagement
- Customer advisory boards discuss responsible AI
- Internal teams contribute to framework improvements
- Security and compliance teams stay involved
- External thought leadership informs best practices