Artificial Intelligence for the Internal Democracy of Political Parties
<|reference_start|>Artificial Intelligence for the Internal Democracy of Political Parties: 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.<|reference_end|>
@article{novelli2024artificial,
    title={Artificial Intelligence for the Internal Democracy of Political Parties},
    author={Claudio Novelli, Giuliano Formisano, Prathm Juneja, Giulia Sandri,
  Luciano Floridi},
    journal={Minds & Machines 34, 36 (2024)},
    year={2024},
    doi={10.1007/s11023-024-09693-x},
    archivePrefix={arXiv},
    eprint={2405.09529},
    primaryClass={cs.CY cs.AI cs.DB cs.LG cs.SI}
}