Design of Ethical AI Frameworks for Sustainable and Adaptive Energy Management Systems - Humadi, Mustafa; Abbas, Haider Hadi; Hilou, Hassan Waryoush; Najm, Nahlah. M. A. D.; Ali, Ammar Abdulkhaleq; Batumalay, M. (Malikussaleh University, Aceh, Indonesia, 2025)
The integration of Artificial Intelligence (AI) in Energy Management Systems changed completely how sustainable infrastructure operates?and is guarded. But the growing independence of AI decision-making presents some serious ethical questions about?fairness, transparency, and accountability. The article introduces a new framework with Ethical AI for Sustainable and Adaptive Energy Management Systems (EAI-SEM) that is designed to combine functional (re)configuration for operational control and ethical governance in centralized: smart buildings and?decentralized: nano-grid settings. The approach incorporates deep reinforcement learning for adaptive control, federated learning for privacy-preserving model updates, and an?integrated Ethics Verification Module for a dynamic assessment of privacy-conformance levels. In experimental simulations over 30-day operation of the smart building and 10-rounds of federated training of the nano-grid, unjust fairness deviation and explainability of the system experienced enhancements, which also indicated?the reduction of carbon dioxide emissions. The?study demonstrated that ethical protocols can be included without impacting on computational efficiency and system responsiveness. Additionally, the federated structure facilitated decentralized ethical responsibility across different actors and thus allowed for the scalable?implementation. The authors verify the possibility of integrating ethics into the computational core of?intelligent energy systems, near from auditing static policies, towards dynamic ethical choices. In the future the process innovation work could be applied to deployments in other infrastructure systems like water?systems and mobility systems, and it provides a reproducible model for the embedding of normative reasoning into AI for infrastructure.