Government by algorithm: Difference between revisions

Revision as of 11:07, 21 April 2025

Revision as of 18:42, 23 April 2025

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[[File:Architecture-of-the-IoT-for-home-care-systems.jpg|thumb|Architecture of the [[IoT]] for home care systems]]

A [[smart city]] is an urban area where collected surveillance data is used to improve various operations. Increase in computational power allows more automated decision making and replacement of public agencies by algorithmic governance.<ref>{{cite journal |last1=Brauneis |first1=Robert |last2=Goodman |first2=Ellen P. |title=Algorithmic Transparency for the Smart City |journal=Yale Journal of Law & Technology |date=1 January 2018 |volume=20 |issue=1 |pages=103 |url=https://www.questia.com/library/journal/1G1-544510684/algorithmic-transparency-for-the-smart-city |access-date=20 September 2020 |archive-date=15 August 2022 |archive-url=https://web.archive.org/web/20220815021439/https://www.gale.com/databases/questia |url-status=dead }}</ref> In particular, the combined use of artificial intelligence and blockchains for [[Internet of things|IoT]] may lead to the creation of [[sustainable]] smart city ecosystems.<ref>{{cite journal |last1=Singh |first1=Saurabh |last2=Sharma |first2=Pradip Kumar |last3=Yoon |first3=Byungun |last4=Shojafar |first4=Mohammad |last5=Cho |first5=Gi Hwan |last6=Ra |first6=In-Ho |title=Convergence of blockchain and artificial intelligence in IoT network for the sustainable smart city |journal=Sustainable Cities and Society |date=1 December 2020 |volume=63 |pages=102364 |doi=10.1016/j.scs.2020.102364 |bibcode=2020SusCS..6302364S |s2cid=225022879 |url=https://www.sciencedirect.com/science/article/abs/pii/S2210670720305850 |access-date=24 March 2021 |issn=2210-6707}}</ref> [[Intelligent street lighting]] in [[Glasgow]] is an example of successful government application of AI algorithms.<ref>{{cite news |last1=Gardner |first1=Allison |title=Don't write off government algorithms – responsible AI can produce real benefits |work=The Conversation |url=https://theconversation.com/dont-write-off-government-algorithms-responsible-ai-can-produce-real-benefits-145895 |access-date=1 April 2021}}</ref> A study of smart city initiatives in the US shows that it requires public sector as a main organizer and coordinator, the private sector as a technology and infrastructure provider, and universities as expertise contributors.<ref>{{cite book |last1=Morrow |first1=Garrett |title=The Robot in City Hall: The Limitations, Structure, and Governance of Smart City Technology Regimes|date=2022 |url=https://www.proquest.com/openview/6785af5e14f63d5e91d47b76337e4aec/1.pdf |via=ProQuest |language=en}}</ref>

The [[cryptocurrency]] millionaire Jeffrey Berns proposed the operation of [[local governments]] in [[Nevada]] by tech firms in 2021.<ref name=nevada>{{cite news |title=Nevada smart city: A millionaire's plan to create a local government |url=https://www.bbc.com/news/world-us-canada-56409924 |access-date=24 March 2021 |work=BBC News |date=18 March 2021}}</ref> Berns bought 67,000 acres (271&nbsp;km²) in Nevada's rural [[Storey County]] (population 4,104) for $170,000,000 (£121,000,000) in 2018 in order to develop a smart city with more than 36,000 residents that could generate an annual output of $4,600,000,000.<ref name=nevada/> Cryptocurrency would be allowed for payments.<ref name=nevada/> Blockchains, Inc. "Innovation Zone" was canceled in September 2021 after it failed to secure enough water<ref>{{Cite web |last=Independent |first=Daniel Rothberg {{!}} The Nevada |title=Blockchains, Inc. withdraws 'Innovation Zone' plan for Storey County |url=http://www.nnbw.com/news/2021/oct/12/blockchains-inc-withdraws-innovation-zone-plan-sto/ |access-date=2022-11-07 |website=www.nnbw.com}}</ref> for the planned 36,000 residents, through water imports from a site located 100 miles away in the neighboring [[Washoe County, Nevada|Washoe County]].<ref name="thenevadaindependent.com">{{Cite web |title=Months before a company lobbied the Legislature to create its own county, it purchased faraway water rights that could fuel future growth |url=https://thenevadaindependent.com/article/months-before-a-company-lobbied-the-legislature-to-create-its-own-county-it-purchased-faraway-water-rights-that-could-fuel-future-growth |access-date=2022-11-07 |website=The Nevada Independent |date=12 February 2021 |language=en}}</ref> A similar water pipeline proposed in 2007 was estimated to cost $100 million and would have taken about 10 years to develop.<ref name="thenevadaindependent.com"/> With additional water rights purchased from Tahoe Reno Industrial General Improvement District, "Innovation Zone" would have acquired enough water for about 15,400 homes - meaning that it would have barely covered its planned 15,000 dwelling units, leaving nothing for the rest of the projected city and its 22 million square-feet of industrial development.<ref name="thenevadaindependent.com"/>

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{{See also|Early warning system}}

[[Tsunami]]s can be detected by [[Tsunami warning system]]s. They can make use of AI.<ref>{{cite web |first=Magdalena |last=Osumi|title=How AI will help us better understand tsunami risks |date=August 16, 2019|url=https://www.preventionweb.net/news/how-ai-will-help-us-better-understand-tsunami-risks |website=www.preventionweb.net}}</ref><ref>{{Cite web|url=https://www.researchgate.net/publication/221057355|title=Artificially Intelligent Tsunami Early Warning System}}</ref> [[Flooding]]s can also be detected using AI systems.<ref>{{cite web|url=https://fortune.com/2019/08/10/artificial-intelligence-wildfires-flooding-rescue/|title=How Artificial Intelligence Could Help Fight Climate Change-Driven Wildfires and Save Lives|website=Fortune}}</ref> [[Wildfire]]s can be predicted using AI systems.<ref>{{cite journal |title=Predictive modeling of wildfires: A new dataset and machine learning approach|first1=Younes Oulad|last1=Sayad|first2=Hajar |last2=Mousannif|first3=Hassan|last3=Al Moatassime|date=March 1, 2019|journal=Fire Safety Journal |volume=104|pages=130–146|doi=10.1016/j.firesaf.2019.01.006|s2cid=116032143|doi-access=free|bibcode=2019FirSJ.104..130S }}</ref><ref>{{Cite web|url=https://www.researchgate.net/publication/261272818|title=Artificial intelligence for forest fire prediction}}</ref> [[Wildfire#Detection|Wildfire detection is possible by AI systems]] (i.e. through satellite data, aerial imagery, and GPS phone personnel position) and can help in the evacuation of people during wildfires,<ref>{{cite journal |last1= Zhao |first1=Xilei |last2=Lovreglio |first2=Ruggiero |last3=Kuligowski |first3=Erica |last4=Nilsson |first4=Daniel |date=April 15, 2020 |title=Using Artificial Intelligence for Safe and Effective Wildfire Evacuations |journal=Fire Technology |volume=57 |issue=2 |pages=483–485 |doi=10.1007/s10694-020-00979-x |s2cid=218801709 |doi-access=free}}</ref> to investigate how householders responded in wildfires<ref>{{Cite journal |last1=Zhao |first1=Xilei |last2=Xu |first2=Yiming |last3=Lovreglio |first3=Ruggiero |last4=Kuligowski |first4=Erica |last5=Nilsson |first5=Daniel |last6=Cova |first6=Thomas J. |last7=Wu |first7=Alex |last8=Yan |first8=Xiang |date=2022-06-01 |title=Estimating wildfire evacuation decision and departure timing using large-scale GPS data |url=https://www.sciencedirect.com/science/article/pii/S136192092200102X |journal=Transportation Research Part D: Transport and Environment |volume=107 |pages=103277 |doi=10.1016/j.trd.2022.103277 |issn=1361-9209|arxiv=2109.07745 |bibcode=2022TRPD..10703277Z }}</ref> and spotting wildfire in real time using [[computer vision]].<ref>{{cite web|url=https://www.cnn.com/2019/12/05/tech/ai-wildfires/index.html |title=How AI is helping spot wildfires faster|author1=Rachel Metz |others=Video by John General |website=CNN|date=5 December 2019 }}</ref><ref>{{cite news |url=https://www.washingtonpost.com/technology/2019/11/06/california-has-million-acres-forest-this-company-is-training-artificial-intelligence-scour-it-all-wildfire/|title=California has 33 million acres of forest. This company is training artificial intelligence to scour it all for wildfire. |first=Peter|last=Holley |newspaper=The Washington Post}}</ref> [[Earthquake warning system|Earthquake detection systems]] are now improving alongside the development of AI technology through measuring seismic data and implementing complex algorithms to improve detection and prediction rates.<ref>{{Cite journal |last1=Mousavi |first1=S. Mostafa |last2=Sheng |first2=Yixiao |last3=Zhu |first3=Weiqiang |last4=Beroza |first4=Gregory C. |date=2019 |title=STanford EArthquake Dataset (STEAD): A Global Data Set of Seismic Signals for AI |journal=IEEE Access |volume=7 |pages=179464–179476 |doi=10.1109/ACCESS.2019.2947848 |s2cid=208111095 |issn=2169-3536|doi-access=free |bibcode=2019IEEEA...7q9464M }}</ref><ref>{{Cite journal |last1=Banna |first1=Md. Hasan Al |last2=Taher |first2=Kazi Abu |last3=Kaiser |first3=M. Shamim |last4=Mahmud |first4=Mufti |last5=Rahman |first5=Md. Sazzadur |last6=Hosen |first6=A. S. M. Sanwar |last7=Cho |first7=Gi Hwan |date=2020 |title=Application of Artificial Intelligence in Predicting Earthquakes: State-of-the-Art and Future Challenges |journal=IEEE Access |volume=8 |pages=192880–192923 |doi=10.1109/ACCESS.2020.3029859 |s2cid=226292959 |issn=2169-3536|doi-access=free |bibcode=2020IEEEA...8s2880B }}</ref><ref>{{Cite web |date=2022-02-09 |title=How Location Intelligence Can Help Protect Lives During Disasters |url=https://ehsdailyadvisor.blr.com/2022/02/how-___location-intelligence-can-help-protect-lives-during-disasters/ |access-date=2024-01-23 |website=EHS Daily Advisor |language=en-US}}</ref> Earthquake monitoring, phase picking, and seismic signal detection have developed through AI algorithms of [[Deep learning|deep-learning]], analysis, and computational models.<ref>{{Cite journal |last1=Mousavi |first1=S. Mostafa |last2=Ellsworth |first2=William L. |last3=Zhu |first3=Weiqiang |last4=Chuang |first4=Lindsay Y. |last5=Beroza |first5=Gregory C. |date=2020-08-07 |title=Earthquake transformer—an attentive deep-learning model for simultaneous earthquake detection and phase picking |journal=Nature Communications |language=en |volume=11 |issue=1 |pages=3952 |doi=10.1038/s41467-020-17591-w |pmid=32770023 |pmc=7415159 |bibcode=2020NatCo..11.3952M |issn=2041-1723}}</ref> [[Locust]] breeding areas can be approximated using machine learning, which could help to stop locust swarms in an early phase.<ref>{{cite journal |last1=Gómez |first1=Diego |last2=Salvador |first2=Pablo |last3=Sanz |first3=Julia |last4=Casanova |first4=Carlos |last5=Taratiel |first5=Daniel |last6=Casanova |first6=Jose Luis |date=August 15, 2018 |title=Machine learning approach to locate desert locust breeding areas based on ESA CCI soil moisture |journal=Journal of Applied Remote Sensing |volume=12 |issue=3 |at=036011 |bibcode=2018JARS...12c6011G |doi=10.1117/1.JRS.12.036011 |doi-access=free |s2cid=52230139}}</ref>

==Reception==