The Moral Algorithm: Navigating Global AI Ethics & Governance

1. The Moral Algorithm: Navigating Global AI Ethics & Governance

GLBC Course Project | February 2026
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Presented by: Davron, Azizbek, Jahongir
The Moral Algorithm:
Navigating Global AI
Ethics & Governance

2.

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Presentation
Objectives &
Agenda
• Goal: Analyze ethical friction between AI and human rights
• Introduction & Global Context
• Research Methodology
• Data Analysis: The 2026 Trust Landscape
• Findings: Corporate & Social Responsibility
• Interactive Audience Discussion

3. Introduction: The 2026 AI Ethics Landscape

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Introduction:
The 2026 AI
Ethics
Landscape
• Evolution: From Chatbots to
Autonomous Agents
• The Conflict: Global innovation
vs. Local ethics
• The Risk: 'Ethical Debt' in the
2026 global market
• Key Question: How do we
communicate trust across
borders?

4. The Global Business Case for Ethics

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The Global
Business
Case for
Ethics
• Consumer Trust: 72% report
boycotting 'Opaque AI'
• Compliance: EU AI Act &
2026 UN Global Digital
Compact
• Branding: Ethics as a
communication strategy for
longevity

5. Research Methodology

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Research
Methodology
• Approach: Mixed
Methods (Qualitative
& Quantitative)
• Quantitative: Metaanalysis of 20242026 Trust Surveys
• Qualitative:
Analysis of Fortune
500 AI Ethics
Charters
• Primary Sources:
IEEE, Gartner, and
UN Policy Papers

6. Visualizing the 'Trust Gap' (Data)

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Visualizing
the 'Trust
Gap' (Data)
• Capability Spike: 300%
increase in AI performance
since 2023
• Trust Decline: 18% drop due
to deepfakes and data leaks
• [Insert Chart: AI Capability
vs. Public Trust]

7. Identifying Algorithmic Bias

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Identifying Algorithmic
Bias
• 60% of recruitment AI still shows
historical bias
• Focus Sectors: Finance (Credit), HR
(Filtering), Media (Deepfakes)
• Necessity of Human-in-the-Loop (HITL)
auditing

8. Analysis Plan & Tools

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Analysis Plan
& Tools
• NVivo: Thematic coding of
corporate ethics policies
• Python (Pandas): Visualizing
bias trends in large datasets
• Focus: Mapping Corporate
Communication vs. Actual
Transparency

9. Key Finding: The 'Black Box' Liability

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Key Finding: The 'Black
Box' Liability
• Legal Risk: Liability for decisions that
cannot be explained
• Solution: Transitioning to Explainable AI
(XAI)
• Benefit: Reducing 'Communication
Friction' with regulators

10. Expected Contributions to GLBC

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Expected
Contributions
to GLBC
• Framework: 3-Step Guide
(Transparency, Redress,
Oversight)
• Trade Impact: Standardizing
Ethical Auditing in trade
agreements
• Leadership: Preparing
managers for AI-driven team
governance

11. Conclusion & Summary

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Conclusion &
Summary
• Summary: AI is the tool; Ethics is
the operator
• Future Outlook: Collaborative
Intelligence over Autonomous
Replacement
• Final Thought: Ethics is the
foundation of global trust

12. Questions To the Audience

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Questions To
the Audience
1. Whose laws apply to a
global AI error: The
developer's or the victim's?
2. Is 'Biometric Productivity
Monitoring' ethical or an
invasion?
3. How do we prevent a
'Digital Ethics Divide' between
rich and poor nations?

13. References

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References
• UNESCO (2025). Recommendation on the Ethics of AI
• McKinsey (2026). The Ethical State of GenAI
• Journal of Business Ethics (2025). Algorithmic Accountability