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 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 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.
Jan et al. (Thu,) studied this question.