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Artificial intelligence is viewed as the important technology production and the social management tools which emphasize providing the neutral and subjective output for users currently.

用戶驅動的數據透視切換

IMPACT WAY

AI CENSUS

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The cognitive bias exhibited by AI systems of the data narratives is not merely an accidental technical flaw, but the manifest of the asymmetry of deep intertwining between global knowledge structures and the capital logic.

The Aboriginal societies, kinship co-parenting is wrongly categorized as single-parent family structure in western technological discourse.

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English consist of the 82%, while Chinese content is significant lower which only accounted for 5% of the sum amount. 

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IBM Watson Oncology had encountered the crisis of roused by apply the outdated patient’s case that creating 61% conflicted recommendations against the current medical guidelines in the lung cancer treatment area

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The ±2.5σ standard deviation threshold criteria which  wrongly  delete as high as 19% while keeping 92% the consumption data of the urban elites since their stability.

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Processing massive natural language processing work would emit the carbon footprint which equivalent to the greenhouse gas emissions of 300 transatlantic flights. But the consumed over 90% of the arithmetic resources cost only satisfied the top 10% of the global population for their computing needs.

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The Hidden Census

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Ahmed, M. et al. (2018a) Torn apart / separados, xpmethod. Available at: https://xpmethod.columbia.edu/torn-apart/volume/1/ (Accessed: 07 August 2025).

Allam, L. et al. (2025) The Killing Times: A massacre map of Australia’s frontier wars, The Guardian. Available at: https://www.theguardian.com/australia-news/ng-interactive/2019/mar/04/massacre-map-australia-the-killing-times-frontier-wars(Accessed: 07 August 2025).

Liu, C. et al. (2018) ‘Using artificial intelligence (watson for oncology) for treatment recommendations amongst Chinese patients with lung cancer: Feasibility study’, Journal of Medical Internet Research, 20(9). doi:10.2196/11087.

Samora, R. and Pera-McGhee, M. (2024) We had an AI attempt to make a data-driven story like we do at the pudding, Can an AI make a data-driven, visual story? Available at: https://pudding.cool/2024/07/ai/ (Accessed: 07 August 2025).

Strubell, E., Ganesh, A. and McCallum, A. (2020) ‘Energy and policy considerations for Modern Deep Learning Research’, Proceedings of the AAAI Conference on Artificial Intelligence, 34(09), pp. 13693–13696. doi:10.1609/aaai.v34i09.7123.

Taylor, J. and Kukutai, T. (eds.) (2016) Indigenous data sovereignty: Toward an agenda. Acton, ACT, Australia: Australian National University Press.

Tubaro, P. et al. (2025) ‘The digital labour of Artificial Intelligence in Latin America: A comparison of Argentina, Brazil, and Venezuela’, Globalizations, pp. 1–16. doi:10.1080/14747731.2025.2465171.

commoncrawl (2023) Statistics of common crawl monthly archives, Statistics of Common Crawl Monthly Archives by commoncrawl. Available at: https://commoncrawl.github.io/cc-crawl-statistics/plots/languages (Accessed: 06 August 2025).

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