The Futures of Work in the Age of Intelligent Machines
Papers are invited for a special issue of the journal Information, Technology & People to highlight research that addresses current and expected futures of work as computer systems with enhanced capacity to reason, aka intelligent machines, take on more and more tasks. As both pundits and scholars increasingly report, intelligent machines are ever more capable of performing tasks that until recently have been the sole purview of humans. Machines can now recognize images or speech, for example, with an ability approaching or even surpassing that of humans. Moreover, these abilities enable them to perform a greater and greater array of useful work, often with increased speed and at less cost. Applications of these abilities are already beginning to affect labor markets and the character and structure of work.
Most of the popular rhetoric on intelligent machines today emphasizes the prospect of people being put out of work by automation, and much of the research centers on technology and its potential for autonomous action. Somewhere in the middle lies a more expansive perspective that we address in this special issue—one that attempts to understand, design, and improve work that it is shared with (rather than simply replaced by) intelligent machines. This approach accords with the focus of the National Robotics Initiative 2.0 (NSF 17–518), for one, which details the need to attend to "robots... that work beside or cooperatively with people.”
Take, for example, the case of intelligent machines in the field of oncology. While it may be feasible to develop an automated system to diagnose skin cancer (Esteva et al., 2017), it is too simplistic to assume all radiologists will therefore be replaced by intelligent automation. First, tasks that can be automated rarely stand in isolation (Chui et al., 2015); indeed, context often shapes tasks. Second, to be practicable, automated diagnostic systems need to fit into the complex work of a medical practice. Someone must order the imaging, image the correct area of the body using the right lighting, explain the diagnosis to the patient, family members, or other doctors (in varied and appropriate ways), bill insurance companies, monitor ongoing performance, defend malpractice suits, and so on. So even if intelligent machines make significant inroads into doctors’ offices, it is unlikely that the entire occupation of radiologists (or oncologists or medical assistants) will be eliminated. Rather, as the World Economic Forum notes, “[intelligent machines] are likely to substitute specific tasks previously carried out as part of these jobs” (2016). To look at the relationship of oncology and intelligent machines expansively, then, is to recognize that while some tasks and skills might be eliminated, others may even see increased demand. Indeed, research has already noted an increase in the demand for social skills (Deming, 2015).
Finally, this special issue solicits papers that offer a convergent research perspective on the futures of work, namely research that draws on multiple disciplinary perspectives and attempts to create novel integrative frameworks and vocabularies. Addressing the challenge of work and intelligent machines expansively requires an engagement with complex questions related to labor, incentives, motivation, cognition, machine learning, human learning, and systems design, among others. It should also attend to various levels of analysis, from the individual to the societal. Only by integrating these different perspectives can we design futures of work that best serve work and workers while simultaneously leveraging the expanded capabilities of intelligent technologies.
Papers for the special issue might address questions including (but not limited to):
• How, when, where, and why are intelligent machines currently affecting markets, occupations, or work practices?
• How can the balance between effective technological application and human worker satisfaction be achieved?
• What anticipatory strategies, e.g., related to worker education and training might be employed to address technologies on the horizon?
• What are possible second- and third-order effects of intelligent machines, both current and on the horizon, and how might these be anticipated and mitigated?
• How and why do impacts on work and workers differ across occupational settings, socio-economic groups, and geographies?
Address questions to the special issue editors:
Kevin Crowston, Syracuse University, [email protected].
Ingrid Erickson, Syracuse University, [email protected]
Jeffrey Nickerson, Stevens Institute of Technology, [email protected]
Key dates (planned):
Submissions open: 1 September 2022
Submissions close: 1 December 2022
Reviews to authors: 1 March 2023
Revisions due: 1 May 2023
Final decisions: 1 June 2023
Final copy due: 1 July 2023
Chui, M., Manyika, J., & Miremadi, M. (2015, November). Four fundamentals of workplace automation. McKinsey Quarterly, 1–9.
Deming, D. J. (2015). The Growing Importance of Social Skills in the Labor Market. National Bureau of Economic Research.
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115–118.
National Science Foundation. (2017). “Convergence Research at NSF.” Available from https://www.nsf.gov/od/oia/convergence/index.jsp
World Economic Forum. (2016). The Future of Jobs: Employment, Skills and Workforce Strategy for the Fourth Industrial Revolution. World Economic Forum.