Explainable Artificial Intelligence Methods to Enhance Transparency and Trust in Digital Deliberation Settings

Ilias Siachos () and Nikos Karacapilidis ()
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Ilias Siachos: Industrial Management and Information Systems Lab, University of Patras, Rion, 26504 Patras, Greece
Nikos Karacapilidis: Industrial Management and Information Systems Lab, University of Patras, Rion, 26504 Patras, Greece

Future Internet, 2024, vol. 16, issue 7, 1-15

Abstract: Digital deliberation has been steadily growing in recent years, enabling citizens from different geographical locations and diverse opinions and expertise to participate in policy-making processes. Software platforms aiming to support digital deliberation usually suffer from information overload, due to the large amount of feedback that is often provided. While Machine Learning and Natural Language Processing techniques can alleviate this drawback, their complex structure discourages users from trusting their results. This paper proposes two Explainable Artificial Intelligence models to enhance transparency and trust in the modus operandi of the above techniques, which concern the processes of clustering and summarization of citizens’ feedback that has been uploaded on a digital deliberation platform.

Keywords: digital deliberation; explainable artificial intelligence; clustering; summarization (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2024
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