Artificial Intelligence for the Internal Democracy of Political Parties

Journal article

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.

Authors: Novelli, C; Formisano, G; Juneja, P; Sandri, G; Floridi, L

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Springer
• Journal: Minds and Machines
• Volume: 34
• Issue: 4
• Article number: 36
• Publication date: 2024-09-04
• Acceptance date: 2024-08-09
• DOI: 10.1007/s11023-024-09693-x
• EISSN: 1572-8641
• Language: English
• Keywords: Machine Learning; Intra-Party Democracy; Artificial Intelligence; Data management; Democracy