🤖 AI Summary
This paper addresses the regulatory compliance challenges faced by edge AI systems under frameworks such as the EU Artificial Intelligence Act, with a focus on dataset compliance as a critical bottleneck. Methodologically, it integrates regulatory mapping analysis, edge-computing–constrained modeling, data governance assessment, and AI lifecycle auditing to systematically identify prevalent compliance barriers across development, deployment, and operation phases. Its primary contributions are threefold: (1) the first end-to-end legal compliance framework specifically designed for edge AI; (2) the formal establishment of dataset compliance as the technical and ethical foundation for trustworthy, transparent, and explainable AI; and (3) the deep integration of ethical requirements into technical implementation pathways. The resulting artifact is a practical, actionable best-practice guideline that enables responsible deployment of embedded AI systems and facilitates regulatory alignment and collaboration.
📝 Abstract
The increasing integration of artificial intelligence (AI) systems in various fields requires solid concepts to ensure compliance with upcoming legislation. This paper systematically examines the compliance of AI systems with relevant legislation, focusing on the EU's AI Act and the compliance of data sets. The analysis highlighted many challenges associated with edge devices, which are increasingly being used to deploy AI applications closer and closer to the data sources. Such devices often face unique issues due to their decentralized nature and limited computing resources for implementing sophisticated compliance mechanisms. By analyzing AI implementations, the paper identifies challenges and proposes the first best practices for legal compliance when developing, deploying, and running AI. The importance of data set compliance is highlighted as a cornerstone for ensuring the trustworthiness, transparency, and explainability of AI systems, which must be aligned with ethical standards set forth in regulatory frameworks such as the AI Act. The insights gained should contribute to the ongoing discourse on the responsible development and deployment of embedded AI systems.