International Journal of Multidisciplinary Research and Growth Evaluation  |  ISSN (Online): 2582-7138  |  Double-Blind Peer Review  |  Open Access  |  CC BY 4.0

Current Issues
     2026:7/3

International Journal of Multidisciplinary Research and Growth Evaluation

ISSN (Online): 2582-7138 | Open Access

Human-in-the-Loop Automation: Redesigning Global Business Processes to Optimize Collaboration between AI and Employees

Full Text (PDF)

Open Access - Free to Download

Download Full Article (PDF)

Alternative download link

Abstract

The implementation of Artificial Intelligence (AI) in the business process worldwide is fast, with potential efficiency improvements never seen before. However, the complete automation oftentimes lacks nuance and the situational sense of judgment, which only a human employee can provide. With the world of business looking to expand its operations both in scale and domain, the resolve to mingle the precision of AI and the intuition and ethical thinking of human beings has never been more important. This essay discusses Human-in-the-Loop (HITL) paradigm, a kind of hybrid approach that preserves human supervision during the automatized processes, as one of the key approaches to designing and governing. Through assessing empirical evidence, theoretical frameworks, and the case studies around the world, the study will be able to determine the best practices on implementing HITL practices to guarantee operational excellence and excellent quality of decisions made, as well as maintaining a workforce through engagement. Using a mixed-methods design, the paper answers practical questions related to task design, regulatory compliance, and cross-cultural adaptability with an eventual recommendation to redesign business processes and human-AI work relationship according to global scalability and resilience.

How to Cite This Article

Abdullateef Okuboye (2022). Human-in-the-Loop Automation: Redesigning Global Business Processes to Optimize Collaboration between AI and Employees . International Journal of Multidisciplinary Research and Growth Evaluation (IJMRGE), 3(1), 1169-1178. DOI: https://doi.org/10.54660/IJMRGE.2022.3.1.1169-1178

Export Citation:

BibTeX RIS EndNote

References

  1. 1. Amershi S, Weld D, Vorvoreanu M, Fourney A, Nushi B, Collisson P, etal. Guidelinesforhuman-AIinteraction. In: Proceedingsofthe2019CHIConferenceon Human Factorsin Computing Systems;2019. p.1-13.
  2. 2. Beer M, Nohria N. Breakingthecodeofchange. Boston: Harvard Business School Press;2000.
  3. 3. Binns R. Fairnessinmachinelearning: lessonsfrompoliticalphilosophy. In: Proceedingsofthe2018 Conferenceon Fairness, Accountability, and Transparency;2018. p.149-59.
  4. 4. Binns R, Veale M, Vanjusticeinalgorithmicdecisions. In: CHIConferenceon Human Factorsin Computing Systems;2018. p.1-14.
  5. 5. Bostrom N, Yudkowsky E. Theethicsofartificialintelligence. In: Cambridge F, editor. The Cambridgehandbookofartificialintelligence. Cambridge: Cambridge University Press;2014. p.316-34.
  6. 6. Braun V, Clarke V. Usingthematicanalysisinpsychology. Qual Res Psychol.2006;3(2\:77-101.
  7. 7. Brennan M, Peterson A, Barnett J. Human-in-the-loopsystemsin Fintech: emerginggovernancechallenges. JFinanc Regul.2020;6(1\:89-102.
  8. 8. Brkan M. AI-supporteddecision-makingandthe EUGDPR: anautomateddecisionisnotadecision. Eur Law J.2021;27(2\:141-58.
  9. 9. Brynjolfsson E, Mc Afee A. Machine, platform, crowd: harnessingourdigitalfuture. New York: W. W. Norton&Company;2017.
  10. 10. CNN. Boeing737 Maxcrashes: humanerrorand Availablefrom: https://www. cnn. com.
  11. 11. Creswell JW, Plano Clark VL. Designingandconductingmixedmethodsresearch.3rded. Thousand Oaks: SAGEPublications;2018.
  12. 12. Davenport TH, Ronanki R. Artificialintelligencefortherealworld. Harv Bus Rev.2018;96(1\:108-16.
  13. 13. Eubanks V. Automatinginequality: howhigh-techtoolsprofile, police, andpunishthepoor. New York: St.
  14. 14. European Commission. Proposalforaregulationonacited2025https://digital-strategy. ec. europa. eu.
  15. 15. Floridi L, Cowls J, Beltrametti M, Chiarello F, Alemanno G, etal. AI4 Peopleanethicalframeworkforagood AIsociety: opportunities, risks, principles, andrecommendations. Minds Mach.2018;28(4\:689-707.
  16. 16. Giacomin J. Whatishumancentreddesign?Des J.2014;17(4\:606-23.
  17. 17. Hoff K, Bashir M. Trustinautomation: integratingempiricalevidenceonfactorsthatinfluencetrust. Hum Factors.2015;57(3\:407-34.
  18. 18. Hofstede G. Dimensionalizingcultures: the Hofstedemodelincontext. Online Read Psychol Cult.2011;2(1\:1-26.
  19. 19. Kahneman D. Thinking, fastandslow. New York: Farrar, Strausand Giroux;2011.
  20. 20. Kotter JP. Leadingchange: whytransformationeffortsfail. Harv Bus Rev.1995;73(2\:59-67.
  21. 21. Lee J, Ardell C, Bagheri B, Kao H. Industrial AIandhuman-centeredmaintenance. Procedia CIRP.2020;93:734-9.
  22. 22. Lee JD, See KA. Trustinautomation: designingforappropriatereliance. Hum Factors.2004;46(1\:50-80.
  23. 23. Mc Kinsey&Company. Thestateof AIin2022https://www. mckinsey. com.
  24. 24. Mittelstadt BD, Allo P, Taddeo M, Wachter S, Floridi L. Theethicsofalgorithms: mappingthedebate. Big Data Soc.2016;3(2\:1-21.
  25. 25. National Instituteof Standardsand Technology(NIST\. AIriskmanagementframework. U. S. Departmentof Commerce;2022.
  26. 26. Norman DA. Thedesignofeverydaythings. Reviseded. New York: Basic Books;2013.
  27. 27. National Transportation Safety Board;2020[cited
  28. 202528. OECD. The OECDframeworkfortheclassificationof AIsystems. Organisationfor Economic Co-operationand Development;2021.
  29. 29. Parasuraman R, Riley V. Humansandautomation: use, misuse, disuse, abuse. Hum Factors.1997;39(2\:230-
  30. 53. International Journalof Multidisciplinary Researchand Growth Evaluationwww. allmultidisciplinaryjournal. com1178|Page
  31. 30. Pasmore WA. Designingeffectiveorganizations: thesociotechnicalsystemsperspective. New York: Wiley;1988.
  32. 31. Pew Research Center. Globalattitudestoward AIandfrom: https://www. pewresearch. org.
  33. 32. Rajpurkar P, Irvin J, Zhu K, Yang B, Mehta H, Duan T, etal. Deeplearningforchestradiographdiagnosis: aretrospectivecomparisonofthe Che XNe Xtalgorithmtopracticingradiologists. Nat Med.2018;24(9\:1325-8.
  34. 33. Rahwan I, Cebrian M, Obradovich N, Bongard J, Bonnefon JF, Breazeal C, etal. Machinebehaviour. Nature.2019;568(7753\:477-86.
  35. 34. Raji ID, Smart A, White RN, Mitchell M, Gebru T, Hutchinson B, etal. Closingthe AIaccountabilitygap: defininganend-to-endframeworkforinternalalgorithmicauditing. In: Proceedingsofthe2020 Conferenceon Fairness, Accountability, and Transparency;2020. p.33-44.
  36. 35. Seeber I, Bittner EAC, Briggs RO, de Vreede GJ, Elkins A, Maier R, etal. Machinesasteammates: aresearchagendaon AIinteamcollaboration. JBus Res.2020;120:274-86.
  37. 36. Sti2020;4(3\:201-2.
  38. 37. Trist EL, Bamforth KW. Somesocialandpsychologicalconsequencesofthelongwallmethodofcoal-getting. Hum Relat.1951;4(1\:3-38.
  39. 38. Umeton R, Lanzola G, Gatti E, Quaglini S. Metricsfortrustinhuman-in-the-loopsystems: challengesandfuturedirections. Front Artif Intell.2020;3:1-12.
  40. 39. Waterson P, Robertson MM, Cooke NJ, Militello L, Roth E, Stanton NA. Definingthesociotechnicalperspectiveforsystemsengineeringdesign. Appl Ergon.2015;45(2\:619-26.
  41. 40. World Economic Forum. Thefutureofjobsreport2022https://www. weforum. org.
  42. 41. Yin RK. Casestudyresearchandapplications: designandmethods.6thed. Thousand Oaks: SAGEPublications;2018.
  43. 42. Zhang B, Dafoe A. Artificialintelligence: Americanhttps://governance. ai.
  44. 43. Zheng VW, Wang Y, Li Y, Yu Z. Human-AIcollaborationframeworks: designingproductiveinteractionmodels. IEEETrans Knowl Data Eng.2021;33(6\:2452-65.

Share This Article: