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Shall androids dream of genocides? How generative AI can change the future of memorialization of mass atrocities.
- Source :
- Discover Artificial Intelligence; 7/18/2023, Vol. 3 Issue 1, p1-17, 17p
- Publication Year :
- 2023
-
Abstract
- The memorialization of mass atrocities such as war crimes and genocides facilitates the remembrance of past suffering, honors those who resisted the perpetrators, and helps prevent the distortion of historical facts. Digital technologies have transformed memorialization practices by enabling less top-down and more creative approaches to remember mass atrocities. At the same time, they may also facilitate the spread of denialism and distortion, attempt to justify past crimes and attack the dignity of victims. The emergence of generative forms of artificial intelligence (AI), which produce textual and visual content, has the potential to revolutionize the field of memorialization even further. AI can identify patterns in training data to create new narratives for representing and interpreting mass atrocities—and do so in a fraction of the time it takes for humans. The use of generative AI in this context raises numerous questions: For example, can the paucity of training data on mass atrocities distort how AI interprets some atrocity-related inquiries? How important is the ability to differentiate between human- and AI-made content concerning mass atrocities? Can AI-made content be used to promote false information concerning atrocities? This article addresses these and other questions by examining the opportunities and risks associated with using generative AIs for memorializing mass atrocities. It also discusses recommendations for AIs integration in memorialization practices to steer the use of these technologies toward a more ethical and sustainable direction. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 27310809
- Volume :
- 3
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- Discover Artificial Intelligence
- Publication Type :
- Academic Journal
- Accession number :
- 165047185
- Full Text :
- https://doi.org/10.1007/s44163-023-00072-6