By Dr. Matthew Cole

September 2019

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There is a rapidly growing literature on the implications of the new wave of automation for work and employment, particularly with regard to artificial intelligence[i]. Automation is a classical theme in the study of work, yet much of the literature has regarded technology as an exogenous force, rather than as an endogenous process shaped by social relations.

 

With regard to automation generally, the mainstream literature[ii] tends to examine automation as either a job creator or job destroyer, rather than as a job augmenter or job competitor in the labour process. With some exceptions[iii], this literature generally does not engage with classical themes from the labour process tradition – for example, the degradation of work thesis.[iv]

 

Furthermore, the macro-level analysis of these studies tends to lack a qualitative dimension. There is an emerging micro-level and qualitative analysis of automation and the quantification of the self,[v] digital labour,[vi] and platform/gig work.[vii] However, there is generally a lack of research on the dynamics of human and artificial intelligence in service work that has previously resisted automation, specifically – creative, emotional, and intellectual work. These types of services have been historically resistant to automation, but now represent a new frontier of technological and social change.

 

How can we examine the dynamics of cooperation and conflict between human and artificial intelligence, particularly the emergence of mutual learning relationships in service work? To begin to answer this question, we can categorise emergent forms of cooperation and conflict between human and artificial intelligence in three ways:

 

  • Human as a backup: here, artificial intelligence is the first-responder in the labour process and humans intervene when the process fails. For example, virtual assistant platforms such as Alexa, Duplex, and Amelia are currently trained and mentored by humans and can perform a number of different tasks including placing orders. Call centres use artificial intelligence to handle caller queries with human operators intervening when technology fails. Financial services use machine learning and deep learning for high-frequency trading as they can respond to market conditions much faster than human traders.

 

  • Human as an overseer: in these cases, artificial intelligence has done all the preparatory work and would be quite capable of completing the task itself, yet due to sensitive ethical issues, the labour process always depends on a human in the last instance. For example, militaries use drones and missile systems that are nearly fully automated, yet a human is present to press the button. Policing and security firms use artificial intelligence in surveillance for facial recognition systems in airports and other high-security zones.

 

  • Human as collaborator: here, artificial intelligence is used as primarily as an aid to human intelligence. For example, doctors rely on diagnostic databases to augment their own capacities, customer service platforms like Stich Fix (an AI stylist) or Sixth Sense (an AI retail assistant) are used to enhance creative labour. Taxi or food delivery services use order and navigation systems that assist drivers and customers.

 

Based on this typology, I want to develop indices of the degree of human-machine cooperation and conflict in service work that had previously resisted automation. The recent rise in the use of artificial intelligence to augment intellectual labour in different  industriespotentially represents a qualitative shift in human-machine relations such that they facilitate mutual learning and augmentation. Particularly interesting are the human-machine dynamics in work situations that normally require human creative, emotional or ethical decision-making capacities to draw out conflict and potential biases. We can address these phenomena with the following questions:

 

 

  1. Does the introduction of intelligent automation to the labour process represent a qualitative shift in the nature of work and employment? What can classical debates around deskilling and technological substitution tell us about the current wave of intelligent automation?

 

 

  1. What are the dynamics of cooperation and conflict between human and artificial intelligence in service work that had previously resisted automation?

 

 

My research into these areas is ongoing.

Matt’s research is being carried out as part of my post-doctoral research fellowship at the Centre for Employment Relations, Innovation and Change (CERIC) at Leeds University Business School. If you and/or your employer would be interested in participating in this study please contact M.Cole@leeds.ac.uk.

Matt is also a research affiliate of Autonomy, coordinator of the IIPPE Political Economy of Work Group and a member of the British Universities Industrial Relations Association.

Endnotes

[i] See Acemoglu, D., Restrepo, P., 2018. Artificial intelligence, automation and work. National Bureau of Economic Research, NBER Working Paper No. 24196; Angrave, D., Charlwood, A., Kirkpatrick, I., Lawrence, M., Stuart, M., 2016. HR and analytics: why HR is set to fail the big data challenge. Human Resource; Moore, P.V., Upchurch, M., Whittaker, X., 2017. Humans and Machines at Work : Monitoring, Surveillance and Automation in Contemporary Capitalism. Palgrave Macmillan US, Cham. https://doi.org/10.1007/978-3-319-58232-0; Neufeind, M., O’Reilly, J., Ranft, F., 2018. Work in the digital age: challenges of the fourth industrial revolution Identifying the challenges for work in the digital age; Nica, E., 2016. Will Technological Unemployment and Workplace Automation Generate Greater Capital-Labor Income Imbalences? Economics, Management and Financial Markets 11, 68; Boyd, R., Holton, R.J., 2018. Technology, innovation, employment and power: Does robotics and artificial intelligence really mean social transformation? Journal of Sociology 54, 331–345. https://doi.org/10.1177/1440783317726591

 

[ii] Autor, D.H., Dorn, D., 2013. The Growth of Low-Skill Service Jobs and the Polarization of the US Labor Market. The American Economic Review 103, 1553–1597. https://doi.org/10.1257/aer.103.5.1553; Frey, C.B., Osborne, M.A., 2013. The Future of Employment: How Susceptible Are Jobs to Computerisation? The Oxford Martin School, Oxford.

 

[iii] Huws, U., 2014. Labor in the global digital economy; Spencer, D., 2017. Work in and beyond the Second Machine Age: the politics of production and digital technologies.

 

[iv] Braverman, H., 1974. Labor and monopoly capital: the degradation of work in the twentieth century. Monthly Review Press, New York.

 

[v] Moore, P.V., 2018. The quantified self in precarity: work, technology and what counts. Routledge, Abingdon, Oxon; Moore, P.V., Upchurch, M., Whittaker, X., 2017. Humans and Machines at Work : Monitoring, Surveillance and Automation in Contemporary Capitalism. Palgrave Macmillan US, Cham. https://doi.org/10.1007/978-3-319-58232-0

 

[vi] Briken, K., Chillas, S., Krzywdzinski, M., 2017. The New Digital Workplace: How New Technologies Revolutionise Work. Red Globe Press; Huws, U., 2014. Labor in the global digital economy.

 

[vii] Gandini, A., 2018. Labour process theory and the gig economy. Human Relations 1872671879000. https://doi.org/10.1177/0018726718790002; Wood, A.J., Graham, M., Lehdonvirta, V., Hjorth, I., 2018. Good Gig, Bad Big: Autonomy and Algorithmic Control in the Global Gig Economy. Work, Employment and Society 95001701878561. https://doi.org/10.1177/0950017018785616