Whose job will AI replace? Here’s why a clerk in Ethiopia has more to fear than one in California

Other

Shafiabady, Niusha. (2023). Whose job will AI replace? Here’s why a clerk in Ethiopia has more to fear than one in California The Conversation Media Group.
AuthorsShafiabady, Niusha
Abstract

Artificial intelligence is changing the world – and one of the main areas it will affect in the short-to-medium term is the workforce.

AI algorithms imitate real-world systems. The more repetitive a system is, the easier it is for AI to replace it. That’s why jobs in customer service, retail and clerical roles are regularly named as being the most at risk.

That doesn’t mean other jobs won’t be affected. The latest advances in AI have shown all kinds of creative work and white-collar professions stand to be impacted to various degrees.

However, there’s one important point that’s usually not addressed in discussions about AI’s impact on jobs. That is: where you work may be as important as what you do.

Current trends and projections suggest people in developing countries, where a higher proportion of jobs involve repetitive or manual tasks, will be the first and most affected.

Year03 Nov 2023
PublisherThe Conversation Media Group
ISSN2201-5639
Web address (URL)https://theconversation.com/whose-job-will-ai-replace-heres-why-a-clerk-in-ethiopia-has-more-to-fear-than-one-in-california-216735
Publisher's version
License
File Access Level
Open
Output statusPublished
Publication dates
Online2023
Publication process dates
Deposited27 Mar 2025
JournalThe Conversation
Open accessOpen access
Permalink -

https://acuresearchbank.acu.edu.au/item/917x3/whose-job-will-ai-replace-here-s-why-a-clerk-in-ethiopia-has-more-to-fear-than-one-in-california

Log in to edit

Download files

Publisher's version
OA_Shafiabady_2023_Whose_job_will_AI_replace_Here.pdf
License: CC BY-ND 4.0
File access level: Open

  • 7
    total views
  • 1
    total downloads
  • 6
    views this month
  • 1
    downloads this month
These values are for the period from 19th October 2020, when this repository was created.

Export as

Related outputs

Efficient energy utilization in smart grids an artificial intelligence perspective
Qadir, Zakria, Khan, Yasir Ali, Rana, Muhammad Tausif Afzal, Din, Fareed Ud and Shafiabady, Niusha. (2025). Efficient energy utilization in smart grids an artificial intelligence perspective. In In Bhatia, Tarandeep Kaur, El Hajjami, Salma, Kaushik, Keshav, Diallo, Gayo, Ouaissa, Mariya and Khan, Inam Ullah (Ed.). Ethical artificial intelligence in power electronics pp. 133-147 CRC Press. https://doi.org/10.1201/9781032648323-9
AI is everywhere – including countless applications you’ve likely never heard of
Shafiabady, Niusha. (2024). AI is everywhere – including countless applications you’ve likely never heard of The Conversation Media Group.
eXplainable Artificial Intelligence (XAI) for improving organisational regility
Shafiabady, Niusha, Hadjinicolaou, Nick, Hettikankanamage, Nadeesha, MohammadiSavadkoohi, Ehsan, Wu, Robert M. X. and Vakilian, James. (2024). eXplainable Artificial Intelligence (XAI) for improving organisational regility. PLoS ONE. 19(4), p. Article e0301429. https://doi.org/10.1371/journal.pone.0301429
Real-time structural health monitoring of bridges using convolutional neural network-based loss factor analysis for enhanced energy dissipation detection
Nguyen, Thanh Q., Vu, Tu B., Shafiabady, Niusha, Nguyen, Thuy T. and Nguyen, Phuoc T.. (2024). Real-time structural health monitoring of bridges using convolutional neural network-based loss factor analysis for enhanced energy dissipation detection. Structures. 70, p. Article 107733. https://doi.org/10.1016/j.istruc.2024.107733
Comparative study of ten machine learning algorithms for short-term forecasting in gas warning systems
Wu, Robert M.X., Shafiabady, Niusha, Zhang, Huan, Lu, Haiyan, Gide, Ergun, Liu, Jinrong and Charbonnier, Clement Franck Benoit. (2024). Comparative study of ten machine learning algorithms for short-term forecasting in gas warning systems. Scientific Reports. 14(1), pp. 1-18. https://doi.org/10.1038/s41598-024-67283-4
Using Artificial Intelligence (AI) to predict organizational agility
Shafiabady, Niusha, Hadjinicolaou, Nick, Din, Fareed Ud, Bhandari, Binayak, Wu, Robert M. X. and Vakilian, James. (2023). Using Artificial Intelligence (AI) to predict organizational agility. PLoS ONE. 18(5), p. Article e0283066. https://doi.org/10.1371/journal.pone.0283066
Using multi-focus group method as an effective tool for eliciting business system requirements : Verified by a case study
Wu, Robert M. X., Wang, Yongwen, Shafiabady, Niusha, Zhang, Huan, Yan, Wanjun, Gou, Jinwen, Shi, Yong, Liu, Bao, Gide, Ergun, Kang, Changlong, Zhang, Zhongwu, Shen, Bo, Li, Xiaoquan, Fan, Jianfeng, He, Xiangqian, Soar, Jeffrey, Zhao, Haijun, Sun, Lei, Huo, Wenying and Wang, Ya. (2023). Using multi-focus group method as an effective tool for eliciting business system requirements : Verified by a case study. PLoS ONE. 18(3), p. Article e0281603. https://doi.org/10.1371/journal.pone.0281603
Identifying presence of cybersickness symptoms using AI-based predictive learning algorithms
Zaidi, Syed Fawad M., Shafiabady, Niusha and Beilby, Justin. (2023). Identifying presence of cybersickness symptoms using AI-based predictive learning algorithms. Virtual Reality. 27(4), pp. 3613-3620. https://doi.org/10.1007/s10055-023-00813-z
V-CarE—A conceptual conceptual design model for providing COVID-19 pandemic awareness : Proposal for a virtual reality design approach to facilitate people with persistent postural-perceptual dizziness
Zaidi, Syed Fawad M., Shafiabady, Niusha, Afifi, Shereen and Beilby, Justin. (2023). V-CarE—A conceptual conceptual design model for providing COVID-19 pandemic awareness : Proposal for a virtual reality design approach to facilitate people with persistent postural-perceptual dizziness. JMIR Research Protocols. 12, p. Article e38369. https://doi.org/10.2196/38369
An FSV analysis approach to verify the robustness of the triple-correlation analysis theoretical framework
Wu, Robert M. X., Zhang, Zhongwu, Zhang, Huan, Wang, Yongwen, Shafiabady, Niusha, Yan, Wanjun, Gou, Jinwen, Gide, Ergun and Zhang, Siqing. (2023). An FSV analysis approach to verify the robustness of the triple-correlation analysis theoretical framework. Scientific Reports. 13(1), p. Article 9621. https://doi.org/10.1038/s41598-023-35900-3
Implementation of transformer-based deep learning architecture for the development of surface roughness classifier using sound and cutting force signals
Bhandari, Binayak, Park, Gijun and Shafiabady, Niusha. (2023). Implementation of transformer-based deep learning architecture for the development of surface roughness classifier using sound and cutting force signals. Neural Computing and Applications. 35(18), pp. 13275-13292. https://doi.org/10.1007/s00521-023-08425-z
Evolving Hybrid partial genetic algorithm classification model for cost-effective frailty screening : Investigative study
Oates, John, Shafiabady, Niusha, Ambagtsheer, Rachel, Beilby, Justin, Seiboth, Chris and Dent, Elsa. (2022). Evolving Hybrid partial genetic algorithm classification model for cost-effective frailty screening : Investigative study. JMIR Aging. 5(4), p. Article e38464. https://doi.org/10.2196/38464
The application of artificial intelligence (AI) techniques to identify frailty within a residential aged care administrative data set
Ambagtsheer, R. C., Shafiabady, N., Dent, E., Seiboth, C. and Beilby, J.. (2020). The application of artificial intelligence (AI) techniques to identify frailty within a residential aged care administrative data set. International Journal of Medical Informatics. 136, p. Article 104094. https://doi.org/10.1016/j.ijmedinf.2020.104094
ST (Shafiabady-Teshnehlab) optimization algorithm
Shafiabady, Niusha. (2018). ST (Shafiabady-Teshnehlab) optimization algorithm. In In Tan, Ying (Ed.). Swarm intelligence : Volume 2 : Innovation, new algorithms and methods pp. 83-110 The Institution of Engineering and Technology. https://doi.org/10.1049/pbce119g_ch4
Acute effects of methadone on EEG power spectrum and event-related potentials among heroin dependents
Motlagh, Farid, Ibrahim, Fatimah, Rashid, Rusdi, Shafiabady, Niusha, Seghatoleslam, Tahereh and Habil, Hussain. (2018). Acute effects of methadone on EEG power spectrum and event-related potentials among heroin dependents. Psychopharmacology. 235(11), pp. 3273-3288. https://doi.org/10.1007/s00213-018-5035-0