Government by algorithm: Difference between revisions

Revision as of 21:07, 14 August 2023

Revision as of 18:57, 24 August 2023

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From 2012, [[NOPD]] started a collaboration with Palantir Technologies in the field of [[predictive policing]].<ref name=verge>{{cite news |last1=Winston |first1=Ali |title=Palantir has secretly been using New Orleans to test its predictive policing technology |url=https://www.theverge.com/2018/2/27/17054740/palantir-predictive-policing-tool-new-orleans-nopd |access-date=23 April 2020 |work=The Verge |date=27 February 2018}}</ref> Besides Palantir's Gotham software, other similar ([[numerical analysis software]]) used by police agencies (such as the NCRIC) include [[SAS (software)|SAS]].<ref>[https://www.vice.com/en/article/neapqg/300-californian-cities-secretly-have-access-to-palantir Vice Motherboard article on Palantir]</ref>

In the fight against money laundering, [[Financial Crimes Enforcement Network|FinCEN]] employs the FinCEN Artificial Intelligence System (FAIS) since 1995.<ref>{{cite journal |last1=Senator |first1=Ted E. |last2=Wong |first2=Raphael W.H. |last3=Marrone |first3=Michael P. |last4=Llamas |first4=Winston M. |last5=Klinger |first5=Christina D. |last6=Khan |first6=A.F. Umar |last7=Cottini |first7=Matthew A. |last8=Goldberg |first8=Henry G. |last9=Wooton |first9=Jerry |title=The FinCEN Artificial Intelligence System: Identifying Potential Money Laundering from Reports of Large Cash Transactions |journal=AAAI |url=https://www.aaai.org/Library/IAAI/1995/iaai95-015.php |access-date=10 September 2022}}</ref><ref>{{cite journalbook |last1=Goldberg |first1=H. G. |last2=Senator |first2=T. E. |title=Agent Technology |chapter=The FinCEN AI System: Finding Financial Crimes in a Large Database of Cash Transactions |journal=Agent Technology: Foundations, Applications, and Markets |date=1998 |pages=283–302 |doi=10.1007/978-3-662-03678-5_15 |chapter-url=https://link.springer.com/chapter/10.1007/978-3-662-03678-5_15 |publisher=Springer |isbn=978-3-642-08344-0 |language=en}}</ref>

National health administration entities and organisations such as AHIMA (American Health Information Management Association) hold [[medical record]]s. Medical records serve as the central repository for planning patient care and documenting communication among patient and health care provider and professionals contributing to the patient's care. In the EU, work is ongoing on a [[European Health Data Space]] which supports the use of health data.<ref>[https://ec.europa.eu/commission/presscorner/detail/en/IP_20_2049 Commission and Germany's presidency of the Council of the EU underline importance of the European Health Data Space]</ref>

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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|url=https://www.preventionweb.net/go/67242|title=Japanese team develops AI-based system to forecast tsunami and damages|website=www.preventionweb.net}}{{Dead link|date=May 2023 |bot=InternetArchiveBot |fix-attempted=yes }}</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}}</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 personnel position) and can help in the evacuation of people during wildfires.<ref>{{cite journal|title=Using Artificial Intelligence for Safe and Effective Wildfire Evacuations |first1=Xilei|last1=Zhao |first2=Ruggiero|last2=Lovreglio|first3=Erica|last3=Kuligowski|first4=Daniel |last4=Nilsson|date=April 15, 2020|journal=Fire Technology|volume=57|issue=2|pages=483–485 |doi=10.1007/s10694-020-00979-x|s2cid=218801709|doi-access=free}}</ref><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 web |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 |via=www.washingtonpost.com}}</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 |url=https://ieeexplore.ieee.org/document/8871127 |journal=IEEE Access |volume=7 |pages=179464–179476 |doi=10.1109/ACCESS.2019.2947848 |s2cid=208111095 |issn=2169-3536|doi-access=free }}</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 |url=https://ieeexplore.ieee.org/document/9218936 |journal=IEEE Access |volume=8 |pages=192880–192923 |doi=10.1109/ACCESS.2020.3029859 |s2cid=226292959 |issn=2169-3536}}</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==