Unveiling public perception of AI ethics: an exploration on Wikipedia data

Mengyi Wei; Yu Feng; Chuan Chen; Peng Luo; Chenyu Zuo; Liqiu Meng

doi:10.1140/epjds/s13688-024-00462-5

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Unveiling public perception of AI ethics: an exploration on Wikipedia data

Journal article

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Unveiling public perception of AI ethics: an exploration on Wikipedia data

Mengyi Wei, Yu Feng, Chuan Chen, Peng Luo, Chenyu Zuo and Liqiu Meng

EPJ data science, Vol.13(1), pp.26-17

12/01/2024

DOI: 10.1140/epjds/s13688-024-00462-5

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Abstract

Artificial Intelligence (AI) technologies have exposed more and more ethical issues while providing services to people. It is challenging for people to realize the occurrence of AI ethical issues in most cases. The lower the public awareness, the more difficult it is to address AI ethical issues. Many previous studies have explored public reactions and opinions on AI ethical issues through questionnaires and social media platforms like Twitter. However, these approaches primarily focus on categorizing popular topics and sentiments, overlooking the public's potential lack of knowledge underlying these issues. Few studies revealed the holistic knowledge structure of AI ethical topics and the relations among the subtopics. As the world's largest online encyclopedia, Wikipedia encourages people to jointly contribute and share their knowledge by adding new topics and following a well-accepted hierarchical structure. Through public viewing and editing, Wikipedia serves as a proxy for knowledge transmission. This study aims to analyze how the public comprehend the body of knowledge of AI ethics. We adopted the community detection approach to identify the hierarchical community of the AI ethical topics, and further extracted the AI ethics-related entities, which are proper nouns, organizations, and persons. The findings reveal that the primary topics at the top-level community, most pertinent to AI ethics, predominantly revolve around knowledge-based and ethical issues. Examples include transitions from Information Theory to Internet Copyright Infringement. In summary, this study contributes to three points, (1) to present the holistic knowledge structure of AI ethics, (2) to evaluate and improve the existing body of knowledge of AI ethics, (3) to enhance public perception of AI ethics to mitigate the risks associated with AI technologies.

Mathematical Methods In Social Sciences

Mathematics

Mathematics, Interdisciplinary Applications

Physical Sciences

Science & Technology

Social Sciences

Social Sciences, Mathematical Methods

Details

Title: Subtitle: Unveiling public perception of AI ethics: an exploration on Wikipedia data
Creators: Mengyi Wei - Technical University of Munich
Yu Feng - Ludwig-Maximilians-Universität München
Chuan Chen - Technical University of Munich
Peng Luo - Technical University of Munich
Chenyu Zuo - ETH Zurich
Liqiu Meng - Technical University of Munich
Resource Type: Journal article
Publication Details: EPJ data science, Vol.13(1), pp.26-17
DOI: 10.1140/epjds/s13688-024-00462-5
ISSN: 2193-1127
eISSN: 2193-1127
Publisher: Springer Nature
Number of pages: 17
Grant note: Swiss Federal Institute of Technology Zurich; ETH Zurich
Language: English
Date published: 12/01/2024
Academic Unit: School of Earth, Environment, and Sustainability
Record Identifier: 9985219297002771

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