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An Integrative Framing Study of the Public Discourse Around AI Ethics /
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
正題名/作者:
An Integrative Framing Study of the Public Discourse Around AI Ethics // Heesoo Jang.
作者:
Jang, Heesoo,
面頁冊數:
1 electronic resource (258 pages)
附註:
Source: Dissertations Abstracts International, Volume: 86-02, Section: B.
Contained By:
Dissertations Abstracts International86-02B.
標題:
Film studies. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31482608
ISBN:
9798383687659
An Integrative Framing Study of the Public Discourse Around AI Ethics /
Jang, Heesoo,
An Integrative Framing Study of the Public Discourse Around AI Ethics /
Heesoo Jang. - 1 electronic resource (258 pages)
Source: Dissertations Abstracts International, Volume: 86-02, Section: B.
This dissertation project explores the landscape of artificial intelligence (AI) ethics public discourse through a comprehensive methodological approach that combines computational tools with qualitative grounded theory. The research aims to identify and analyze the frames around AI ethics available to the public, referred to as issue culture, investigating the dominance of specific frames within AI ethics discussions and the power dynamics shaping these conversations. Methodologically, the study employs Latent Dirichlet Allocation (LDA) topic modeling and community detection techniques to uncover latent themes and relationships within datasets from news media, congressional records, and Big AI companies' documents collected over a nine-year period from January 2015 to December 2023. The data collection involved compiling extensive corpora of text from these diverse sources, ensuring a robust and representative sample of public discourse on AI ethics. From each actor, top documents from identified topics were sampled for qualitative analysis. Through qualitative analysis, eleven distinct frames were identified, including the Inevitable AI Frame, the Ends-oriented AI Frame, the Human-AI Collaboration Frame, the AI Race Frame, the Racial Injustice in AI Frame, the National Security and Defense Preparedness Frame, the Bipartisan Cooperation Frame, the Digital Transparency Frame, the Strategic Ecosystem Frame, the Responsible AI Frame, and the "Efficiency is Sustainable" Frame. The findings reveal the prevalence and influence of these frames across different actors, with certain frames being particularly dominant across actors. These frames emphasized themes such as collaboration, competition, goal-orientation, and ethics. This research advances the methodological framework for studying public discourse by integrating computational methods with qualitative insights, providing a comprehensive analysis of the framing processes, in this case within AI ethics discussions. The study's significance lies in its contribution to a deeper understanding of AI ethics, highlighting the role of powerful actors in framing the public discourse of AI ethics. The results and discussions offer critical insights for policymakers, journalists, and the public, which could be used to advance a more balanced and inclusive dialogue on AI ethics. Future research directions include expanding the scope of datasets to include more diverse sources of public discourse and integrating normative theory through an abductive approach to grounded theory.
English
ISBN: 9798383687659Subjects--Topical Terms:
1179264
Film studies.
Subjects--Index Terms:
Artificial intelligence ethics
An Integrative Framing Study of the Public Discourse Around AI Ethics /
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This dissertation project explores the landscape of artificial intelligence (AI) ethics public discourse through a comprehensive methodological approach that combines computational tools with qualitative grounded theory. The research aims to identify and analyze the frames around AI ethics available to the public, referred to as issue culture, investigating the dominance of specific frames within AI ethics discussions and the power dynamics shaping these conversations. Methodologically, the study employs Latent Dirichlet Allocation (LDA) topic modeling and community detection techniques to uncover latent themes and relationships within datasets from news media, congressional records, and Big AI companies' documents collected over a nine-year period from January 2015 to December 2023. The data collection involved compiling extensive corpora of text from these diverse sources, ensuring a robust and representative sample of public discourse on AI ethics. From each actor, top documents from identified topics were sampled for qualitative analysis. Through qualitative analysis, eleven distinct frames were identified, including the Inevitable AI Frame, the Ends-oriented AI Frame, the Human-AI Collaboration Frame, the AI Race Frame, the Racial Injustice in AI Frame, the National Security and Defense Preparedness Frame, the Bipartisan Cooperation Frame, the Digital Transparency Frame, the Strategic Ecosystem Frame, the Responsible AI Frame, and the "Efficiency is Sustainable" Frame. The findings reveal the prevalence and influence of these frames across different actors, with certain frames being particularly dominant across actors. These frames emphasized themes such as collaboration, competition, goal-orientation, and ethics. This research advances the methodological framework for studying public discourse by integrating computational methods with qualitative insights, providing a comprehensive analysis of the framing processes, in this case within AI ethics discussions. The study's significance lies in its contribution to a deeper understanding of AI ethics, highlighting the role of powerful actors in framing the public discourse of AI ethics. The results and discussions offer critical insights for policymakers, journalists, and the public, which could be used to advance a more balanced and inclusive dialogue on AI ethics. Future research directions include expanding the scope of datasets to include more diverse sources of public discourse and integrating normative theory through an abductive approach to grounded theory.
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http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31482608
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