Published July 25, 2024
| Version v1
Technical note
Open
Supplementary materials for "Resistance Against Manipulative AI: key factors and possible actions"
Authors/Creators
-
1.
Warsaw University of Technology
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2.
University of Warsaw
Description
Technical appendix, code and data for the "Resistance Against Manipulative AI: key factors and possible actions" article.
Files
code_and_data.zip
Files
(1.5 MB)
| Name | Size | Download all |
|---|---|---|
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md5:f89fa8c5b5752ce15556fa88e9691c89
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1.2 MB | Preview Download |
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md5:284645da3b5169fd90f587da10baa7f7
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331.3 kB | Preview Download |
Additional details
Related works
- Is supplement to
- Preprint: arXiv:2404.14230 (arXiv)
References
- W. H. DuBay. Smart Language: Readers, Readability, and the Grading of Text. 2007
- Y.-T. Seih, S. Beier, and J. W. Pennebaker. Development and examination of the linguistic category model in a computerized text analysis method. Journal of Language and Social Psychology, 2017
- V. P. Ta, R. L. Boyd, S. Seraj, et al. An inclusive, real-world investigation of persuasion in language and verbal behavior. Journal of Computational Social Science, 2022
- A. B. Warriner, V. Kuperman, and M. Brysbaert. Norms of valence, arousal, and dominance for 13,915 English lemmas. Behavior research methods, 2013
- D. A. Hanauer, Y. Liu, Q. Mei, F. J. Manion, U. J. Balis, and K. Zheng. Hedging their mets: the use of uncertainty terms in clinical documents and its potential implications when sharing the documents with patients. In AMIA Annual Symposium Proceedings, 2012
- P. Wilczyński, W. Mieleszczenko-Kowszewicz, and P. Biecek. Resistance against manipulative ai: key factors and possible actions. arXiv preprint arXiv:2404.14230, 2024