Government by Algorithm: Artificial Intelligence in Federal Administrative Agencies, a Case of USA

Authors

  • Rubina Shaheen
  • Mir Aimal Kasi

DOI:

https://doi.org/10.47672/ejt.641

Keywords:

Artificial intelligence, Machine learning, AI toolkits, Administrative agencies

Abstract

The report gives a presents use of artificial intelligence in few administrative agencies. In-depth thematic analysis of some institution, have been conducted to review the current trends. In thematic analysis, 12 institutions have been selected and described the details of the institutions using artificial intelligence in different departments. These analyses yielded five major findings. First, the government has a wide application of Artificial Intelligence toolkit traversing the federal administrative and state. Almost half of the federal agencies evaluated (45%) has used AI and associated machine learning (ML) tools. Also, AI tools are already enhancing agency strategies in  the full span of governance responsibilities, such as keeping regulatory assignments bordering on market efficiency, safety in workplace, health care, and protection of the environmental, protecting the privileges and benefits of the government ranging from intellectual properties to disability, accessing, verifying and analyzing all risks to public  safety and health, Extracting essential data from the data stream of government including complaints by consumer and the communicating with citizens on their rights, welfare, asylum seeking and business ownership. AI toolkit owned by government span the complete scope of Artificial Intelligence techniques, ranging from conventional machine learning to deep learning including natural language and image data. Irrespective of huge acceptance of AI, much still has to be done in this area by the government. Recommendations also discussed at the end.

Downloads

Download data is not yet available.

Author Biographies

Rubina Shaheen

 

Institute of Management Sciences, University of Balochistan, Quetta.

 

 

 

 

Mir Aimal Kasi

 

Institute of Management Sciences, University of Balochistan, Quetta.

 

 

References

Bajandas, F.F. & Ray, G.K. (2018). Admin. Conference of the U.S., Implementation and Use of Electronic Case Management Systems in Federal Agency Adjudication.

Chouldechova, A., & Roth, A. (2018), The Frontiers of Fairness in Machine Learning, Cornell U. https://arxiv.org/ abs/1810.08810.

CIRA. (2016). Cira was developed within the Office of Risk Assessment at the Division of Economic and Risk Analysis (DERA). See DERA - Office of Risk Assessment, Sec. & Exch. Comm'n, https://www.sec.gov/page/dera_ora_page. "Corporate issuers" develop and sell securities to finance their operations.

Dep't of Homeland Sec (2016), Biometric Pathway: Transforming Air Travel, Version 3.0 1 (Dec. 1, 2016), https://epic.org/foia/dhs/cbp/ biometric-entry-exit/Biometric-Pathway.pdf.

Desai, R.D. & Kroll, J.A. (2017), Trust But Verify: A Guide to Algorithms and the Law, 31 Harv. J.L. & Tech. 21 (noting that systems require "ongoing monitoring and evaluation to ensure the model remains accurate given that the real world changes")

Donepudi, P. K. (2015). Crossing point of artificial intelligence in cybersecurity. American journal of trade and policy, 2 (3), 121-128. https://doi.org/10.18034/ajtp.v2i3.493

Donepudi, P. K. (2018b). Application of artificial intelligence in automation industry. Asian journal of applied sciences and engineering, 7 (1), 7-20. http://doi.org/10.5281/zenodo.4146232

FCC. (2017). The foundation's DearFCC tool used custom, automatically generated text to allow human users to "craft a unique comment" on the FCC's net neutrality proposal with "just two clicks." Rainey Reitman, Electronic Government by Algorithm: Artificial Intelligence in Federal Administrative Agencies 112 Frontier Foundation, Launching DearFCC: The Best Way to Submit Comments to the FCC about Net Neutrality (May 8, 2017), https://www.eff. org/deeplinks/2017/05/launching-dearfcc-best-way-submit-commentsfcc-about-net-neutrality

Fingas, J. (2018). Chinese Facial Recognition System Confuses Bus Ad with a Jaywalker, Engadget (Nov. 22, 2018), https://www.engadget. com/2018/11/22/chinese-facial-recognition-confuses-bus-ad-withjaywalker/

Form, 10-K, Sec. & Exch. Comm'n, https://www.sec.gov/ fast-answers/answers-form10khtm. html (last modified Nov. 2, 2016); Fast Answers: Form 10-Q, Sec. & Exch. Comm'n, https://www.sec.gov/fastanswers/answersform10qhtm.html (last modified Sept. 2, 2011).

Garvie, C., Bedoya, A. & Frankle, J. (2016), The Perpetual Line-Up: Unregulated Police Face Recognition in America, Geo. L. Ctr. on Privacy & Tech. (Oct. 18, 2016), https://www.perpetuallineup.org.

Serenko, A. (2010). The development of an AI journal ranking based on the revealed preference approach. Journal of Informetrics. 4 (4): 447-459. doi:10.1016/j.joi.2010.04.001.

Shapiro, S.C. (1992). Artificial Intelligence. In Shapiro, Stuart C. (ed.). Encyclopedia of Artificial Intelligence (2nd ed.). New York: John Wiley. pp. 54-57. ISBN 978-0-471-50306-4. Archived from the original on 1 February 2016. Retrieved 29 May 2009.

Downloads

Published

2021-01-03

How to Cite

Shaheen, R. ., & Kasi, M. . (2021). Government by Algorithm: Artificial Intelligence in Federal Administrative Agencies, a Case of USA. European Journal of Technology, 5(1), 1–15. https://doi.org/10.47672/ejt.641

Issue

Section

Articles