Artificial Intelligence for the Internal Democracy of Political Parties
Minds & Machines 34, 36 (2024); https://link.springer.com/article/10.1007/s11023-024-09693-x
Minds and Machines, volume 34, issue 4, 2024[10.1007/s11023-024-09693-x]
26 Pages Posted: 15 Apr 2024 Last revised: 30 Nov 2024
Date Written: March 30, 2024
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 partial data collection, rare updates, and significant resource demands. To address these issues, the article suggests that specific data management and Machine Learning techniques, such as natural language processing and sentiment analysis, can improve the measurement and practice of IPD.
Keywords: Artificial Intelligence, Democracy, Intra-Party Democracy, Machine Learning, Data management
Suggested Citation: Suggested Citation
, Minds and Machines, volume 34, issue 4, 2024[10.1007/s11023-024-09693-x], Available at SSRN: https://ssrn.com/abstract=4778813 or http://dx.doi.org/10.1007/s11023-024-09693-x