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 Floridi
Ultimo
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.
2024
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].
Claudio Novelli, Giuliano Formisano, Prathm Juneja, Giulia Sandri, Luciano Floridi
File in questo prodotto:
File Dimensione Formato  
Artificial Intelligence for the Internal Democracy of Political Parties.pdf

accesso aperto

Descrizione: First Online
Tipo: Versione (PDF) editoriale
Licenza: Licenza per Accesso Aperto. Creative Commons Attribuzione (CCBY)
Dimensione 930.36 kB
Formato Adobe PDF
930.36 kB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/981055
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact