Artificial Intelligence for the Internal Democracy of Political Parties

被引：0

作者：

Novelli, Claudio ^{[1
,2
]}

Formisano, Giuliano ^{[3
,4
]}

Juneja, Prathm ^{[3
]}

Sandri, Giulia ^{[5
]}

Floridi, Luciano ^{[1
,2
]}

机构：

[1] Univ Bologna, Dept Legal Studies, Via Zamboni 27-29, I-40126 Bologna, Italy

[2] Yale Univ, Digital Eth Ctr, 85 Trumbull St, New Haven, CT 06511 USA

[3] Univ Oxford, Oxford Internet Inst, 1 St Giles, Oxford OX1 3JS, England

[4] Univ Oxford, Nuffield Coll, New Rd, Oxford OX1 1NF, England

[5] Univ Libre Bruxelles, Ctr Etud Vie Polit CEVIPOL, Ave Jeanne 44, B-1050 Brussels, Belgium

来源：

MINDS AND MACHINES | 2024年 / 34卷 / 04期

关键词：

Artificial Intelligence; Democracy; Intra-Party Democracy; Machine Learning; Data management; CANDIDATE SELECTION; INTRAPARTY POLITICS; TEXT ANALYSIS; MODEL;

D O I：

10.1007/s11023-024-09693-x

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页数：26

共 50 条

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