Abstract AI-driven anomaly detectors in 5G renewable energy IoT and industrial systems lack unified governance: they operate opaquely, exhibit protocol-class bias, and expose training data to inference attacks. This paper presents the Ethical AI Governance Framework (EAGF), which maps four EU AI Act pillars, transparency (C), fairness (RP/FPRP), privacy (P), and accountability (A) to computable engineering metrics that are jointly governed within one training-and-deployment lifecycle: fairness and privacy are co-optimized via a Pareto-guided multi-objective procedure with domain-adaptive fairness loss selection, transparency is structurally controlled through clarity-triggered pruning, and accountability is audited post hoc, with all four scores aggregated into a composite Trust Index (TI). Evaluated across two domains: on a biometric task (10,021 images, ten seeds), EAGF raises TI by \(+38.97\%\) (\(0.565\rightarrow 0.785\)), improves recall parity by \(+15.1\%\), and enhances privacy by \(+18.8\%\); on the real-world Edge-IIoTset intrusion-detection benchmark (157,800 samples, five seeds), EAGF achieves \(+69.3\%\) TI gain (\(0.358\rightarrow 0.606\)) and \(+56.4\%\) FPR parity improvement, with only \(+0.2\) ms forward-pass inference overhead. Joint multi-pillar governance substantially outperforms model-level-only approaches across both domains; the accountability infrastructure contributes a large and explicitly quantified fraction of total TI gains, underscoring that governance readiness requires both algorithmic and operational investments. Acknowledgements The authors thank the contributors of the Edge-IIoTset dataset and the open-source community for supporting reproducible and transparent AI research. Author information Authors and Affiliations Corresponding author Ethics declarations Ethics approval and consent to participate This study uses publicly available datasets and does not involve direct human subject experimentation. All datasets were used in accordance with their respective licenses and ethical guidelines. Competing interests The authors declare no competing interests. Additional information Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Supplementary Information Rights and permissions Open Access This article is licensed under a Creative