Overview
What Is This Framework?
This is the final deliverable from AIML 510: Responsible AI at Indiana Wesleyan University — a capstone that integrates every prior assignment into one cohesive, deployment-ready framework for a fictional aviation AI company named AeroMind Intelligence.
AeroMind's four AI systems — Predictive Maintenance AI, Pilot Fatigue Risk Scoring, Crew Scheduling Optimization, and Safety Incident Pattern Detection — each carry unique ethical risks that this framework addresses through nine principles, eight governance roles, a phased implementation timeline, and detailed guidelines for bias, explainability, privacy, and long-term impact.
The framework is grounded in NIST AI RMF, NIST SP 1270, GDPR, the EU AI Act, and FAA regulations — and integrates Biblical ethics (Philippians 4:8; Romans 15:1-2) as a foundation for empowerment and authentic AI design.
Ethical Foundation
Nine Guiding Principles
The original six principles from Workshop 1 were expanded through the course to include three customer-facing dimensions surfaced by case studies including Google's "Dear Sydney" ad and the CompanionAI Elara deployment.
AI Lifecycle
Eight-Stage Development Lifecycle
Every AeroMind AI system passes through all eight stages — including a formal Ethics Board Go/No-Go review and a documented disposition protocol before the first line of production code is written.
- Ethics use case review
- Privacy Impact Assessment
- Stakeholder mapping
- Use case charter
- Provenance audit
- Data Card initiated
- Bias pre-screening
- PII scrubbing
- Fairness constraints
- Feature proxy audit
- Model Card initiated
- Differential privacy
- Fairness metric suite
- FMEA analysis
- Red-team testing
- 90-day shadow-mode
- Go / Conditional / No-Go
- All conditions resolved
- Override protocol certified
- CAEO sign-off
- Crew explainability guide
- KPI baseline active
- FAA/GDPR filing
- Training data tags
- Monthly drift detection
- Quarterly fairness audit
- Annual re-certification
- Data lineage audit
- Triggers in Model Card
- Crew notification + data deletion
- Successor evaluated
- No zombie models
Risk Guidelines
Three Critical Risk Areas
Every AI output must be understandable
XAI tools (SHAP, LIME, InterpretML) integrated into CI/CD pipeline. Crew comprehension ≥80% before deployment. Model Cards published publicly for all systems. Right to explanation honored within 15 days of request. Fiddler AI monitors explanation quality drift in production.
NIST SP 1270 taxonomy applied
Six bias types addressed per use case: historical, representation, measurement, aggregation, feedback loop, and automation bias. Microsoft Fairlearn and IBM AIF360 integrated. Quarterly demographic parity audits with 80% rule enforcement. Proxy attribute audit required before feature selection.
US and EU dual compliance
Four-tier privacy classification: Internal / Confidential / Restricted. Biometric data (fatigue scoring) at Restricted tier with 2-year maximum retention. GDPR Art. 9 compliance for EU crew. EU AI Act high-risk classification for fatigue scoring and scheduling. FAA data retention per 14 CFR 121.380.
Implementation
12-Month Phased Rollout
References
Prioritized RAI References
Twelve APA-formatted sources organized by priority of application — the primary daily-use references for responsible AI practice.
Full Presentation
15-Slide Framework Deck
The complete AeroMind Intelligence Responsible AI Framework — all 15 slides from the AIML 510 capstone presentation, including governance charts, lifecycle diagrams, bias guidelines, metrics dashboards, and prioritized references.
Full Framework Presentation
The complete 15-slide AeroMind RAI Framework is available as a PowerPoint deck with detailed charts, metrics, and lifecycle documentation.