JoWUA

Abstract

The implementation of Artificial Intelligence (AI) into systems for managing energy raises substantial difficulties with regard to the problems associated with accountability and regulatory compliance. These challenges impede the implementation of such technologies in domains where there is much bureaucracy in place. In this paper, an attempt is made to fill a crucial void in the area of combining AI optimizations with determinism necessary for legal auditing. The approach presented here introduces an architectural solution aimed at making governance an integral part of the system architecture. This is achieved by employing such mechanisms as data pipelines versioning, model ephemerality, and a deterministic execution environment. The efficacy of this approach is proven through a discrete-event simulation performed in Python using DVC and ONNX. Important statistical considerations revealed in the research confirm the proposed architecture’s success in achieving exact identity (100%) in decision replays in several rounds of assessments, thereby ruling out all forms of stochastic fluctuations. From the statistical findings, it is clear that through the incorporation of machine-readable constraints in the inference process, the framework sustains low latency during the process of verifying the regulation. Moreover, the inclusion of immutability audit trails guaranteed 100% integrity of historical data and prevented any form of fraudulent actions, such as backdating and tampering. In essence, it is evident from the findings of the research that focusing on deterministic inference and proper verification artifacts, instead of real-time inference learning, can ensure AI-enabled energy systems meet regulatory requirements.