A Transformer-Based Model for the Automatic Detection of Administrative Burdens in Transposed Legislative Documents

Abstract

Legislative impact assessment (LIA) can be defined as the process performed by governments and legislative bodies to evaluate the potential effects of proposed policies or directives before they are implemented. This assessment typically covers various aspects (including economic, social, and environmental impacts) and is designed to ensure that policy proposals are well-founded, transparent, and that potential impacts are thoroughly examined before decisions are made. This process is nowadays performed by human experts and requires a significant amount of time. It is also characterized by some subjectivity that makes it difficult for citizens and companies to perceive the process as a transparent one. Moreover, public administration services responsible for LIA recognize significant difficulties in performing a timely and effective impact assessment exercise due to the lack of human and financial resources. To answer this call, this paper presents an artificial intelligence-based system to automatizing part of the impact assessment process, with the specific objective of detecting administrative burdens from transposed EU legislation. The system is built on a fine-tuned, transformer-based architecture leveraging transfer learning, making it an innovative tool for automating legislative impact assessment. Comprehensive testing on transposed European legislation demonstrated that the system significantly enhances efficiency and accuracy in what has traditionally been a complex and time-consuming task.

Keywords

• legislative impact assessment
• natural language processing
• transformers
• deep learning