A Unified Model for Ethical, Transparent, and Explainable AI in Large-Scale Organizations

Abstract

Artificial intelligence has become foundational to enterprise operations across sectors—from customer engagement and decision support to risk management and real-time automation. While adoption delivers measurable operational gains, it also introduces new governance challenges stemming from opacity, bias, and unclear accountability. This article introduces the Ethical Lifecycle Governance Framework (ELGF), a unified implementation model for ethical, transparent, and explainable AI across organizational environments. ELGF incorporates five execution pillars: transparency, human oversight, validation, continuous monitoring, and accountability, mapped directly onto the AI system lifecycle from data intake through retirement. The model provides organizations with actionable mechanisms rather than abstract policy recommendations, ensuring measurable accountability across program stakeholders. Because the framework is industry-agnostic, organizations can adopt it without changing their existing architecture or operational models. When implemented, ELGF reduces regulatory exposure, enhances stakeholder confidence, and accelerates innovation by enabling responsible deployment of high-impact AI systems. The combined outcome is operational growth aligned with ethical principles rather than in conflict with them.

Executive Summary

AI now influences access to financial services, healthcare eligibility, employment decisions, and essential digital services, making governance no longer optional. The proposed Ethical Lifecycle Governance Framework operationalizes responsible AI deployment by linking policy to execution workflows. Rather than treating ethics as a compliance checkpoint, ELGF embeds governance into every phase of the AI lifecycle. This approach ensures that fairness, transparency, interpretability, and accountability are continuously tested, validated, and traceable. By institutionalizing these controls, organizations not only reduce risk but also scale AI initiatives confidently and sustainably.