Human augmentation, not replacement: A research agenda for AI and robotics in the industry

Sarah Dégallier-Rochat, Mascha Kurpicz-Briki, Nada Endrissat, Olena Yatsenko, Sarah Dégallier-Rochat, Mascha Kurpicz-Briki, Nada Endrissat, Olena Yatsenko

No abstract available

Keywords: artificial intelligence; augmented intelligence; complementary cooperation; human-machine interaction; robotics.

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
The four types of workflows: Manual work, full automation, worker empowerment and worker mechanization. The two axes show the strengths of both humans and machines and the characteristics of the workflow associated with different forms of human-machine interaction.

References

    1. Arntz M., Gregory T., Zierahn U. (2016). The risk of automation for jobs in oecd countries: A comparative analysis. OCED Soc. Employ. Migr. Work. Pap., 1–34. 10.1787/1815199X
    1. Autor D., Salomons A. (2018). Is automation labor-displacing? Productivity growth, employment, and the labor share. National Bureau of Economic Research. Working Paper Series. 10.3386/w24871
    1. Barbieri L., Chiara M., Piva M., Marco V. (2019). Testing the employment impact of automation, robots and AI: A survey and some methodological issues. IZA – Institute of Labor Economics. Working Paper 12612. IZA Discussion Papers. Available at: (Accessed: 23 August 2022).
    1. Braga A., Logan R. K. (2019). AI and the singularity: A fallacy or a great opportunity? Information 10 (2), 73. 10.3390/info10020073
    1. Bringsjord S., Bringsjord A., Bello P. (2012). “Belief in the singularity is fideistic,” in Singularity hypotheses: A scientific and philosophical assessment. (Berlin, Heidelberg: Springer The Frontiers Collection; ), 395–412. 10.1007/978-3-642-32560-1_19
    1. Brynjolfsson E., McAfee A. (2011). Race against the machine: How the digital revolution is accelerating innovation, driving productivity, and irreversibly transforming employment and the economy. Marina Del Rey, California, United States: Digital Frontier Press.
    1. Bui L. (2020). Asian roboticism: Connecting mechanized labor to the automation of work. Perspect. Glob. Dev. Technol. 19 (1–2), 110–126. 10.1163/15691497-12341544
    1. Bundy A. (2017). Preparing for the future of artificial intelligence. AI Soc. 32 (2), 285–287. 10.1007/s00146-016-0685-0
    1. Clegg C. W. (2000). Sociotechnical principles for system design. Appl. Ergon. 31 (5), 463–477. 10.1016/S0003-6870(00)00009-0
    1. Dahlin E. (2019). Are robots stealing our jobs? Socius. 5, 237802311984624. 10.1177/2378023119846249
    1. Daugherty R., Wilson H. J. (2018). Human + machine: Reimagining work in the age of AI. Boston, Massachusetts: Harvard Business Review Press.
    1. De Stefano V. (2018). Negotiating the algorithm”: Automation, artificial intelligence and labour protection. Comp. Labor Law Policy J. 41. Rochester, NY. 10.2139/ssrn.3178233
    1. Draeger J., Müller-Eiselt R. (2019). Wir und die intelligenten maschinen: Wie algorithmen unser leben bestimmen und wir sie für uns nutzen können. Auflage. München: Deutsche Verlags-Anstalt.
    1. Dworschak B., Zaiser H. (2014). Competences for cyber-physical systems in manufacturing – first findings and scenarios. Procedia CIRP 25, 345–350. 10.1016/j.procir.2014.10.048
    1. Eubanks V. (2019). in Automating inequality: How high-tech tools profile, police, and punish the poor. First Picador edition (New York: Picador St. Martin’s Press; ).
    1. European Agency for Safety and Health at Work (2022). Artificial intelligence for worker management: An overview | safety and health at work EU-OSHA. Available at: (Accessed: July 16, 2022).
    1. European Commission (2017). Attitudes towards the impact of digitisation and automation on daily life: Report. Brussels: European Commission. LU: Publications Office. Available at: (Accessed: July 18, 2022).
    1. Fernandez-Macias E., Bisello M. (2020). A taxonomy of tasks for assessing the impact of new technologies on work. JRC Working Papers on Labour, Education and Technology. Joint Research Centre Seville site. Available at: (Accessed: 16 July 2022).
    1. Frey C. B., Osborne M. A. (2017). The future of employment: How susceptible are jobs to computerisation? Technol. Forecast. Soc. Change 114, 254–280. 10.1016/j.techfore.2016.08.019
    1. Geraci R. M. (2008). Apocalyptic AI: Religion and the promise of artificial intelligence. J. Am. Acad. Relig. 76 (1), 138–166. 10.1093/jaarel/lfm101
    1. Goertzel B. (2007). Human-level artificial general intelligence and the possibility of a technological singularity: A reaction to ray kurzweil’s the singularity is near, and McDermott’s critique of kurzweil. Artif. Intell. 171 (18), 1161–1173. 10.1016/j.artint.2007.10.011
    1. Goldberg K. (2015). Robotics: Countering singularity sensationalism. Nature 526 (7573), 320–321. 10.1038/526320a
    1. Gunasekaran A., Yusuf Y., Geyi D., Papadopoulos T., Kovvuri D. (2019). Agile manufacturing: An evolutionary review of practices. Int. J. Prod. Res. 57 (15–16), 5154–5174. 10.1080/00207543.2018.1530478
    1. Hertweck C., Heitz C., Loi M. (2021). “On the moral justification of statistical parity,” in Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, Virtual Event Canada, March 3 - 10, 2021, 747–757. ACM. 10.1145/3442188.3445936
    1. Holm J. R., Lorenz E., Nielsen P. (2020). Work organization and job polarization. Res. Policy 49 (8), 104015. 10.1016/j.respol.2020.104015
    1. Huws U. (2014). Labor in the global digital economy: The cybertariat comes of age. New York, United States: NYU Press.
    1. IEEE (2022). What is augmented intelligence? - IEEE digital reality. Available at: (Accessed June 24, 2022).
    1. Janjić V., Todorović M., Jovanović D. (2020). Key success factors and benefits of kaizen implementation. Eng. Manag. J. 32 (2), 98–106. 10.1080/10429247.2019.1664274
    1. Jankauskaitė V., Christenko A., Paliokaitė A. (2022). Artificial intelligence for worker management: Existing and future regulations | safety and health at work EU-OSHA. Available at: (Accessed: July 16, 2022).
    1. Klenert D., Fernandez-Macias E., Antón J. I. (2020). Do robots really destroy jobs? Evidence from Europe. JRC Working Papers on Labour, Education and Technology 2020–01. Joint Research Centre Seville site. Available at: (Accessed: 16 July 2022).
    1. Kurzweil R. (2005). The singularity is near: When humans transcend biology. New York, US: Penguin Publishing Group.
    1. Leicht-Deobald U., Busch T., Schank C., Weibel A., Simon S., Isabelle W., et al. (2019). The challenges of algorithm-based HR decision-making for personal integrity. J. Bus. Ethics 160, 377–392. 10.1007/s10551-019-04204-w
    1. Levine D. I. (2019). Automation as part of the solution. J. Manag. Inq. 28 (3), 316–318. 10.1177/1056492619827375
    1. Lu Y., Sastre J., Chand S., Wang L. (2021). Humans are not machines—anthropocentric human–machine symbiosis for ultra-flexible smart manufacturing. Engineering 7 (6), 734–737. 10.1016/j.eng.2020.09.018
    1. Macias E., Hurley J., Bisello M. (2016). What do Europeans do at work? A task-based analysis. Luxembourg: Publications Office of the European Union. 10.2806/12545
    1. Mindell D. A. (2015). Our robots, ourselves: Robotics and the myths of autonomy. New York, United States: Penguin Publishing Group.
    1. Moore P. V. (2019). “OSH and the future of work: Benefits and risks of artificial intelligence tools in workplaces,” in Digital human modeling and applications in health, safety, ergonomics and risk management. Human body and motion. Editor Duffy V. G. (Cham: Springer International Publishing Lecture Notes in Computer Science; ), 292–315. 10.1007/978-3-030-22216-1_22
    1. Murray A., Rhymer J., Sirmon D. G. (2021). Humans and technology: Forms of conjoined agency in organizations. Acad. Manage. Rev. 46 (3), 552–571. 10.5465/amr.2019.0186
    1. Natale S., Ballatore A. (2020). Imagining the thinking machine: Technological myths and the rise of artificial intelligence. Convergence. 26 (1), 3–18. 10.1177/1354856517715164
    1. National Science and Technology Council (2016). Preparing for the future of artificial intelligence. Available at: (Accessed: July 16, 2022).
    1. O’Neil C. (2016). in Weapons of math destruction: How big data increases inequality and threatens democracy. First edition (New York: Crown; ).
    1. Parker S. K., Grote G. (2022). Automation, algorithms, and beyond: Why work design matters more than ever in a digital world. Appl. Psychol. 71 (4), 1171–1204. 10.1111/apps.12241
    1. Romero D., Stahre J., Taisch M. (2020). The Operator 4.0: Towards socially sustainable factories of the future. Comput. Industrial Eng. 139, 106128. 10.1016/j.cie.2019.106128
    1. Tan Q., Tong Y., Wu S., Li D. (2019). Anthropocentric approach for smart assembly: Integration and collaboration. J. Robotics 2019, 1–8. 10.1155/2019/3146782
    1. Villani V., Lorenzo S., Julia N., Alexander M., Cesare F. (2018). MATE robots simplifying my work: The benefits and socioethical implications. IEEE Robot. Autom. Mag. 25 (1), 37–45. 10.1109/MRA.2017.2781308
    1. Zimmermann M. (2008). The singularity: A crucial phase in divine self-actualization? Cosmos Hist. J. Nat. Soc. Philosophy 4, 347.

Source: PubMed

3
Abonneren