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Pour plus de transparence dans l'analyse automatique des consultations ouvertes : leçons de la synthèse du Grand Débat National

Aurélien Bellet 1 Pascal Denis 1 Rémi Gilleron 1 Mikaela Keller 1 Nathalie Vauquier 1
1 MAGNET - Machine Learning in Information Networks
Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189
Abstract : Faced with the limits of representative democracy, digital public consultations provide an opportunity for citizens to contribute their opinions and ideas and for policy makers to involve the population more closely in the public decision making process. The design and deployment of such public consultations pose well-known issues related to potential biases in the questions or in the representativeness of the participants. In this article, we consider the novel issues that arise from the use of artificial intelligence methods to automatically analyze contributions in natural language. Conducting such analyses constitutes a difficult problem for which many approaches (relying on various assumptions and models) exist. Considering the responses to the open-ended questions of the French "Grand Débat National" as a case study, we show that it is impossible to reproduce the results of the official analysis commissioned by the government. In addition, we identify a number of implicit and arbitrary choices in the official analysis that cast doubts on some of its results. We show also that different methods can lead to different conclusions. Our study highlights the need for greater transparency in the automatic analyses of public consultations so as to ensure reproducibility and public confidence in their results. We conclude with suggestions for improving digital public consultations and their analysis so that they encourage participation and become useful tools for public debate.
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https://hal.inria.fr/hal-02860659
Contributor : Aurélien Bellet <>
Submitted on : Friday, October 23, 2020 - 7:00:16 PM
Last modification on : Wednesday, March 3, 2021 - 11:40:53 AM

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Aurélien Bellet, Pascal Denis, Rémi Gilleron, Mikaela Keller, Nathalie Vauquier. Pour plus de transparence dans l'analyse automatique des consultations ouvertes : leçons de la synthèse du Grand Débat National. 2020. ⟨hal-02860659v2⟩

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