Abstract :

Artificial Intelligence (AI) Is Reshaping The Global Economy And Redefining The Relationship Between Innovation And Accountability. Across Industries Such As Finance, Healthcare, And Retail, Organizations Rely On AI To Streamline Processes, Personalize Consumer Experiences, And Generate Predictive Insights. However, This Transformative Power Introduces Profound Challenges In Governance, Fairness, And Societal Trust. Traditional Data Governance Frameworks, Designed Primarily For Compliance And Static Systems, Cannot Address The Complexity Of Dynamic, Probabilistic AI Ecosystems. This Paper Introduces AI-Driven Ethical Governance (AIDEG), A Next-generation Framework That Positions Governance As Ethical Infrastructure Rather Than A Compliance Afterthought. The AIDEG Model Integrates Four Foundational Pillars: (1) Bias Audits To Measure And Mitigate Unfair Outcomes, (2) Human-in-the-Loop (HITL) Checkpoints For Oversight In Critical Decisions, (3) Provenance-Aware Data To Enable Transparency And Explainability, And (4) Continuous Monitoring For Real-time Compliance And Anomaly Detection. Using Case Studies From Finance, Healthcare, And E-commerce, We Illustrate How Stewardship Practices Operationalize These Pillars To Safeguard Vulnerable Populations, Reduce Systemic Risks, And Reinforce Organizational Legitimacy. We Also Present An Eight-pillar Stewardship Framework That Expands AIDEG Into A Holistic Model For Cross-industry Implementation. The Contributions Of This Work Are Threefold: (1) Establishing Governance As A Proactive Enabler Of Trustworthy AI, (2) Demonstrating How Stewardship Transforms Ethical Principles Into Operational Practice, And (3) Providing A Roadmap For Enterprises And Regulators To Align Innovation With Accountability. Index Terms Artificial Intelligence, Data Governance, Ethical AI, Compliance, Data Stewardship, Bias Audits, Human-in-the-Loop, Provenance-Aware Data, Continuous Monitoring, AI Governance

Published:

09-10-2025

Issue:

Vol. 25 No. 10 (2025)

Page Nos:

17-21

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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