Digital Amnesia And Algorithmic Memory: Reconstructing The Past In The Age Of Big Data Archives
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Background. The exponential growth of digital data and algorithmic curation has transformed how societies construct, preserve, and remember the past. The phenomenon of digital amnesia the tendency to outsource memory to digital systems reveals a paradox of modern knowledge: while more information is archived than ever before, human capacity for contextual recollection diminishes.
Purpose. This study investigates how algorithmic mechanisms within big data archives reconstruct historical narratives and shape collective memory in the digital age. The research aims to analyze the epistemological and ethical implications of algorithmic memory, focusing on how automated retrieval, ranking, and personalization systems mediate historical knowledge and cultural continuity
Method. A qualitative multi-case analysis was conducted on digital archival platforms and algorithmic recommendation systems using interpretive content analysis and critical data studies methodology.
Results. The findings show that algorithmic archives not only preserve information but actively curate and reinterpret history through patterns of visibility and omission. The findings indicate that memory in the age of big data is not neutral but performative constructed through computational decisions that privilege certain narratives while marginalizing others.
Conclusion. The study concludes that the digital era demands a critical redefinition of archival literacy, emphasizing the need for transparency, human oversight, and ethical design in algorithmic systems. Understanding digital amnesia thus becomes essential to safeguarding cultural memory and ensuring that the reconstruction of the past remains plural, accountable, and inclusive.
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