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J-GLOBAL ID:202402258782209678   Reference number:24A0341864

The Political Biases of ChatGPT

ChatGPTの政治的バイアス【JST・京大機械翻訳】
Author (1):
Material:
Volume: 12  Issue:Page: 148  Publication year: 2023 
JST Material Number: U7278A  ISSN: 2076-0760  Document type: Article
Article type: 短報  Country of issue: Switzerland (CHE)  Language: ENGLISH (EN)
Abstract/Point:
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JST classification (2):
JST classification
Category name(code) classified by JST.
Applied psychology  ,  Other information processing 
Reference (17):
  • 2006 Political Ideology Selector a Free Politics Selector. n.d. Available online: http://www.selectsmart.com/plus/select.php?url=ideology (accessed on 25 February 2023). Adamopoulou, Eleni, and Lefteris Moussiades. 2020. Chatbots: History, Technology, and Applications. Machine Learning with Applications 2: 100006.
  • Ain, Qurat Tul, Mubashir Ali, Amna Riaz, Amna Noureen, Muhammad Kamran, Babar Hayat, and Aziz Ur Rehman. 2017. Sentiment Analysis Using Deep Learning Techniques: A Review. International Journal of Advanced Computer Science and Applications (IJACSA) 8.
  • American Enterprise Institute-AEI (blog). n.d. Are Colleges and Universities Too Liberal? What the Research Says About the Political Composition of Campuses and Campus Climate. Available online: https://www.aei.org/articles/are-colleges-and-universities-too-liberal-what-the-research-says-about-the-political-composition-of-campuses-and-campus-climate/ (accessed on 21 January 2023). Archive, View Author, and Get Author RSS Feed. 2021. Twitter employees give to Democrats by wide margin: Data. Data Shows Twitter Employees Donate More to Democrats by Wide Margin. Available online: https://nypost.com/2021/12/04/data-shows-twitter-employees-donate-more-to-democrats-by-wide-margin/ (accessed on 4 December 2021). Bender, Emily M., Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell. 2021. On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜. In FAccT ’21: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency. New York: Association for Computing Machinery, pp. 610-23.
  • Cowgill, Bo, and Catherine Tucker. 2017. Algorithmic Bias: A Counterfactual Perspective. NSF Trustworthy Algorithms 3.
  • Dabre, Raj, Chenhui Chu, and Anoop Kunchukuttan. 2020. A Survey of Multilingual Neural Machine Translation. ACM Computing Surveys 53: 99:1-99:38.
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