LegiGPT: Party Politics and Transport Policy with Large Language Model

TL;DR

This paper presents LegiGPT, a framework combining large language models and explainable AI to analyze how political ideologies influence transportation legislation, revealing key factors like sponsor characteristics and district demographics.

Contribution

Introduces a novel LLM-based framework with XAI techniques to analyze legislative proposals and uncover political influences on transportation policymaking.

Findings

Political party affiliation significantly affects legislative outcomes.

Sponsor characteristics and district demographics are key determinants.

Bipartisan contributions are evident through different engagement strategies.

Abstract

Given the significant influence of lawmakers' political ideologies on legislative decision-making, analyzing their impact on transportation-related policymaking is of critical importance. This study introduces a novel framework that integrates a large language model (LLM) with explainable artificial intelligence (XAI) to analyze transportation-related legislative proposals. Legislative bill data from South Korea's 21st National Assembly were used to identify key factors shaping transportation policymaking. These include political affiliations and sponsor characteristics. The LLM was employed to classify transportation-related bill proposals through a stepwise filtering process based on keywords, sentences, and contextual relevance. XAI techniques were then applied to examine the relationships between political party affiliation and associated attributes. The results revealed that the number and proportion of conservative and progressive sponsors, along with district size and electoral population, were critical determinants shaping legislative outcomes. These findings suggest that both parties contributed to bipartisan legislation through different forms of engagement, such as initiating or supporting proposals. This integrated approach offers a valuable tool for understanding legislative dynamics and guiding future policy development, with broader implications for infrastructure planning and governance.