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.
Claudio Novelli, G.F. (2024). Artificial Intelligence for the Internal Democracy of Political Parties. MINDS AND MACHINES, 34(First Online), 1-26 [10.1007/s11023-024-09693-x].
Artificial Intelligence for the Internal Democracy of Political Parties
Claudio Novelli
Primo
;Luciano FloridiUltimo
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.File | Dimensione | Formato | |
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