Ethical AI Integration in Water Utilities: A Governance Framework from Onboarding to Full Digital Workspace Adoption

This article presents a structured ethical governance framework for responsible large language model (LLM) and AI tool adoption in public water and wastewater utilities. Developed from the integration experience of SJWD Water District (Spartanburg, South Carolina), the framework addresses the full AI adoption lifecycle, from initial staff onboarding through integration of AI tools into structured digital project workspaces. The framework organizes ethical obligations across four domains: transparency and disclosure, equity and community protection, accuracy and verification with human override requirements, and data security and information boundaries. A five-phase adoption lifecycle model guides utilities from awareness through mature, organization-wide governance. A companion Assessment and Governance Scorecard provides a self-evaluation instrument with 25+ assessment items mapped to each domain and lifecycle phase. The work draws on established governance frameworks including the NIST AI Risk Management Framework (AI RMF 1.0), UNESCO's Recommendation on the Ethics of Artificial Intelligence, Harvard's responsible AI principles, and sector-specific guidance from the American Water Works Association, the Water Research Foundation (Project 5321), and the aiWATERS framework (Vekaria et al., 2024). Peer-reviewed water sector research on AI hallucination risks, algorithmic bias, and pipe failure modeling informs the verification and human-in-the-loop requirements. The framework is designed for public water/wastewater systems and is particularly attentive to NIST Cybersecurity Framework alignment, OT/ICS data boundaries, and the public health accountability obligations that distinguish water utility AI governance from general enterprise AI adoption guidance.