Abstract
Today, there are massive contents online that are delivered, promoted and even generated by data-driven AI. Among them, so-called "post-truth" websites and videos feature inauthentic and pseudo-academic historical recognition. The fact that "post-truth" or alternative historical views are now getting popular is now discussed, but there has been no (or little) discussion that relates "post-truth" historical views to the AI. The author is now starting up a research program on the relationship between data-driven AI and historical recognition and here he tries to set a preliminary theory to think of AI nature and its result in the realm of historical views, especially among non-elite people. In his view, what makes AI special in the study of historical views is that AI islands people from authentic thoughts. Also, data-driven AI is often based on commercial purposes, not academic concepts. This gap makes dialogues difficult between people holding "post-truth" views and those holding legitimate views. It is often said that AI technology is making the world "flat." In other words, the gaps between elite/academic contents and non-elite/non-academic contents are now obscure in the modern cyberspace. However, this suggestion does not explain how certain views are chosen by certain people. For example, experts often say that people lacking information literacy enjoy "post-truth" contents. This does not show why legitimate ones are rejected, though they do not "distinguish" them.
Author Information
Kentaro Okawara, Institute for International Strategy and Information Analysis, Inc., Japan
Paper Information
Conference: PCAH2023
Stream: History/Historiography
This paper is part of the PCAH2023 Conference Proceedings (View)
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To cite this article:
Okawara K. (2023) Preliminary Theory on Relationship Between Data-Driven AI and Historical Recognition ISSN: 2758-0970 The Paris Conference on Arts & Humanities 2023 Official Conference Proceedings https://doi.org/10.22492/issn.2758-0970.2023.17
To link to this article: https://doi.org/10.22492/issn.2758-0970.2023.17
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