📄 Version: 1.0 | 🏛 Issued by: ULA AI Governance Team | 📅 Date: March 2025
1. Introduction
The Uncommon Leadership Academy (ULA) is committed to ethical, transparent, and responsible AI governance in alignment with Singapore’s Model AI Governance Framework (MAIGF). This document outlines ULA’s AI compliance policies, ensuring fairness, accountability, and transparency in the deployment of AI-powered coaching solutions.
2. AI Governance and Ethical Oversight
✅ AI Ethics and Compliance Officer Appointed:
- Oversees AI governance, compliance, and ethical AI use.
- Manages risk escalation and policy enforcement.
✅ AI Code of Conduct Implemented:
- AI is used as an augmentation tool, not a replacement for human coaching.
- Coaching insights remain non-directive, unbiased, and supportive.
✅ AI Risk and Oversight Framework in Place:
- Defines acceptable AI coaching use cases (e.g., feedback, learning prompts, leadership development).
- Implements AI output review mechanisms for human oversight.
3. AI Transparency and Explainability
✅ Explainability Dashboard Integrated:
- AI coaching insights include confidence levels, data sources, and alternative recommendations.
- AI outputs are reviewed to prevent black-box decision-making.
✅ AI User Disclosures Implemented:
- Every AI coaching session includes a clear disclaimer:
“This AI-generated coaching insight is for guidance purposes only. Leadership decisions should involve human judgment.”
✅ Human-in-the-Loop Supervision:
- AI never provides employment-related coaching decisions (e.g., promotions, performance evaluations).
- AI insights are reviewed by certified ULA leadership coaches.
4. AI Bias and Fairness Safeguards
✅ Quarterly AI Bias Audits Conducted:
- AI outputs are tested for gender, cultural, and industry bias.
- Adjustments made to improve diversity in leadership insights.
✅ Diverse AI Training Data Sources:
- AI models incorporate leadership styles across different industries, genders, and cultural backgrounds.
- Regular updates ensure balanced and inclusive coaching feedback.
✅ Fairness Protocols Embedded in AI Models:
- AI-generated insights avoid reinforcing stereotypes.
- Coaching recommendations provide alternative viewpoints.
5. Data Privacy and PDPA Compliance
✅ Explicit User Consent and Data Protection:
- All AI coaching participants must agree to ULA’s data use policies before engaging with AI.
- Users can opt out of AI data processing at any time.
✅ Data Anonymization and Retention Policy:
- AI models do not retain personal coaching data—only generalized insights for model improvements.
- Personal coaching history is not stored beyond a 90-day period.
✅ Secure Data Storage and Cross-Border Compliance:
- AI coaching data is encrypted and stored on PDPA-compliant servers.
- No personal coaching insights are shared without explicit client approval.
6. AI Trust and Stakeholder Engagement
✅ Public AI Trust and Accountability Framework Established:
- ULA engages corporate clients, leadership experts, and data scientists in AI governance discussions.
- AI impact assessments are conducted with stakeholder input.
✅ AI Training and Transparency for Executives:
- ULA hosts AI coaching education sessions to help leaders understand how AI insights are generated.
- AI governance updates are publicly shared with enterprise clients.
✅ Certification of MAIGF Compliance:
- ULA adheres to Singapore’s Model AI Governance Framework and commits to annual audits.
- Compliance documentation is updated every 12 months to reflect best practices.
7. Certification of Compliance
📌 This document certifies that Uncommon Leadership Academy (ULA) has implemented AI governance practices aligned with Singapore’s Model AI Governance Framework.
📜 Issued by:
🔹 [Name], AI Ethics and Compliance Officer
🔹 Uncommon Leadership Academy (ULA)
📅 Date of Issue: March 2025
📆 Next Review: March 2026
🔗 For more details, visit: https://uncommon-leadership.com/ai-code-of-conduct/
🌍 Ethical AI for Leadership Development – The ULA Commitment 🚀
This compliance framework reinforces ULA’s dedication to responsible AI deployment, data privacy, and leadership integrity.