Ethics and Governance in the Age of Generative Artificial Intelligence: A Lifecycle Engineering Perspective

Abstract

ABSTRACT Trustworthy Generative Artificial Intelligence (GenAI) is often treated as a model property addressed through better training data, alignment, evaluation or guardrails. We argue instead that trustworthiness is a lifecycle governance capability that must be engineered, evidenced, monitored and adapted across data acquisition, training and alignment, safe inference, public deployment and post‐deployment use. We develop a lifecycle engineering framework integrating four governance layers – technical, organizational, human‐centred and regulatory – and translate ethical principles and major governance instruments into stage‐specific controls, responsibilities, evidence artefacts, metrics and review triggers. The framework is constructed through a structured conceptual synthesis of peer‐reviewed literature, NIST AI RMF, ISO/IEC 42001, ISO/IEC 23894, ISO/IEC 42005, ALTAI, the EU AI Act and complementary security and agentic‐governance guidance. A source register, seven‐field extraction schema, explicit mapping rules, worked extraction example and weighting sensitivity analysis make the construction logic inspectable. A public‐facing retrieval‐augmented generation case and a contrastive agentic‐system stress test illustrate operationalization through measurable indicators, responsible actors, review triggers and capability boundaries without being presented as independent empirical validation. We further address hallucination, prompt injection, privacy leakage, overreliance, unsafe tool use, memory, planners and multi‐agent workflows. The perspective positions trustworthy GenAI as an evidence‐driven, adaptive socio‐technical capability rather than a one‐time model or compliance claim.

The authors' abstract, as published at the source. Expert Systems, 2026 · DOI ↗