用可解释的人工智能方法提高数字审议环境中的透明度和信任度

Future Internet Pub Date : 2024-07-06 DOI:10.3390/fi16070241
Ilias Siachos, Nikos I. Karacapilidis
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引用次数: 0

摘要

近年来,数字审议一直在稳步发展,使来自不同地理位置、不同意见和专业知识的公民都能参与决策过程。旨在支持数字审议的软件平台通常会因提供大量反馈信息而导致信息超载。虽然机器学习和自然语言处理技术可以缓解这一弊端,但其复杂的结构使用户不愿相信其结果。本文提出了两个可解释人工智能模型,以提高上述技术工作方式的透明度和信任度,它们涉及对上传到数字审议平台的公民反馈进行聚类和汇总的过程。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Explainable Artificial Intelligence Methods to Enhance Transparency and Trust in Digital Deliberation Settings
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.
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