Artificial Intelligence for the Internal Democracy of Political Parties

TL;DR

This paper explores how AI and machine learning can improve the measurement and practice of internal democracy within political parties, addressing limitations of traditional methods and highlighting associated risks.

Contribution

It proposes specific AI techniques like NLP and sentiment analysis to enhance IPD measurement and practice, offering a novel approach to intra-party democracy assessment.

Findings

AI can improve IPD measurement accuracy

ML techniques enable real-time monitoring of democratic processes

Risks include data privacy and potential manipulation

Abstract

The article argues that AI can enhance the measurement and implementation of democratic processes within political parties, known as Intra-Party Democracy (IPD). It identifies the limitations of traditional methods for measuring IPD, which often rely on formal parameters, self-reported data, and tools like surveys. Such limitations lead to the collection of partial data, rare updates, and significant demands on resources. To address these issues, the article suggests that specific data management and Machine Learning (ML) techniques, such as natural language processing and sentiment analysis, can improve the measurement (ML about) and practice (ML for) of IPD. The article concludes by considering some of the principal risks of ML for IPD, including concerns over data privacy, the potential for manipulation, and the dangers of overreliance on technology.