EAGF: a four-pillar ethical AI governance framework for trustworthy cybersecurity in 5G renewable energy IoT systems

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.