Call for Papers
The relationship of work to technology has long been studied (e.g., Barley, 1986; Orlikowski, 1992; Trist & Bamforth,
1951), from the roboticization of factory lines (e.g., Argote et al., 1983; Grint & Woolgar, 2013; Smith & Carayon,
1995) to the integration of information and computing technology into knowledge work (e.g., Hanseth et al., 2006; Leonardi
& Bailey, 2008; Osterlund & Carlile, 2005). As more and more digital technology becomes elemental to modern forms
of work, it is sometimes difficult to separate tasks from tools, procedures from platforms. Today, not only is work primarily
digital and computational, but it is fast becoming algorithmic with the introduction of artificial intelligence into existing
procedures and practices (Brynjolfsson & McAfee, 2014). For instance, radiologists can now leverage artificial intelligence
to analyze patients’ scans instead of relying on their trained eyes alone; these machines, using intelligent algorithms, are
reported to have a higher rate of tumor recognition than even the most well-trained experts (Aerts, 2017; Prevedello et al.,
Noting that there are more and more instances of organizations utilizing artificial intelligence for strategic and operational ends, this sub-theme seeks to better understand these relationships by drawing in empirical scholarship that studies work at this particular human-technology frontier. Incumbent in our desire to convene this conversation are three driving questions:
- Where and how is artificial intelligence being used in contemporary organizations?
- How do these examples help us understand shifts in work practices (i.e., are artificial agents new collaborators, embedded technical constraints, something else entirely)?
- How can enquiries into to working with smart agents reveal what is intrinsically human about modern forms of work?
Artificial intelligence (AI) is a current buzzword in business, but it is a technology that has a long history (McCorduck et al., 1977). In some ways a simple calculator displays ‘intelligence’ in its seemingly cognitive ability to calculate sums rapidly. Yet, today’s reference to the term tends to connote the predictive, rather than the mere processing, power of computation (Chen et al., 2012). Of course, prediction is still a function of processing, but more importantly it is also derivative of the analysis of great stores of past data. These digital traces of the past, when run through powerful machines, reveal patterns. It is these patterns that make up the ingredients of algorithms, which are essentially recipes linking past patterns to potential future patterns. AI occurs in our daily lives everyday when, for example, Amazon recommends books that you might like based on a current selection. Scale this up a bit and you have the example of an autonomous vehicle – a machine that is able to not only see links between Item A and B, but to string a multitude of these relations together and act on them in real time, essentially simulating a human driver who can navigate a complex terrain. The sophistication of the ‘intelligence’ of an autonomous vehicle extends beyond a simple recommendation; instead, it is a result of both predictive power and also machine learning, a computational process whereby a computer learns from environmental feedback. As this feedback comes in, the machine ‘learns’ and gradually improves its operations, ad infinitum.
The intersection of work and artificial intelligence is occurring along a complex spectrum, ranging from things such as the increased use of recommender systems in decision sequences (as hinted at in the Amazon example above) to the incorporation of fully fledged intelligent machines, as in the case of autonomous vehicles upending the jobs of truck drivers or robots conducting surgery. Of course, these variations mirror the wide diversity of work tasks today, but they also reflect the information infrastructures (Bowker et al., 2009; Monteiro & Hanseth, 1996) in which the AI is embedded. While it is conceptually powerful to think of the direct relationship between artificial intelligence and work, rarely do they come together without a mediator. These intermediaries provide platforms for necessary activities to run, they help to integrate disparate technologies with one another, and, when functioning properly, they fade into the background and become embedded in the norms and rules that govern an organization or a culture. To a financial analyst, the practice of utilizing AI may occur within the use of predictive analytics package on a organizationally-mandated data platform – perhaps one that optimizes a complex set of portfolios by visualizing them in such a way that a quick decision can be rendered easily. A truck driver, on the other hand, has quite a different experience of AI. Not only is he or she enveloped by AI in material form, but experientially these drivers are likely limited to a narrow set of options well before the engine is even turned on. Is the driver then an agent of the machine and the analyst a collaborator? These are not only questions of task design, perceived efficiency, and financial optimization but also of a worker’s agency and the boundaries in which they are intended (or allowed) to act.
In recent years information infrastructures have become more widely studied, with a particular interest in the ways that their inherent digital extensibility supports generativity and innovation (e.g., Forman et al., 2014; Yoo et al., 2012). Less well studied, however, is the way that information infrastructures encode certain practices because of their reliance on algorithms and artificial intelligence. We see this emphasis in our proposed sub-theme as a way to take up the mantle of prior work on infrastructures, but also to provide a forum, in line with the general theme of the annual convening, to consider how AI may be challenging (or enlightening) organizations via the increased reliance on and organization of work via information infrastructures.
We encourage submissions that address the broad subject of automation and work from an equally broad array of disciplinary scholars. We invite papers that deal with (but are not limited to) the following topic areas:
AI in the collective
AI knowledge work
AI now and then
Algorithmic phenomena in the organization of work
Breakdowns in AI and work
Designing AI-Human practices
Dynamic relationships between AI and humans
Methodological implication of algorithmic phenomena
Nature of coordination and collaboration in the age of the “smart machine”
Predictions in practice
Roboticization and hybrid agency
Sociomaterial theorizing about new forms of work
- Aerts, H.J.W.L. (2017): “Data Science in Radiology: A Path Forward.” Clinical Cancer Research, 24 (3), 532–534.
- Argote, L., Goodman, P.S., & Schkade, D. (1983): “The Human Side of Robotics: How Workers React to a Robot.” Sloan Management Review, 24 (3), 31–41.
- Barley, S.R. (1986): “Technology as an Occasion for Structuring: Evidence from Observations of CT Scanners and the Social Order of Radiology Departments.” Administrative Science Quarterly, 31 (1), 78–108.
- Bowker, G.C., Baker, K., Millerand, F., & Ribes, D. (2009): “Toward Information Infrastructure Studies: Ways of Knowing in a Networked Environment.” In: J. Hunsinger, L. Klastrup & M. Allen (eds.): International Handbook of Internet Research. Dordrecht: Springer, 97–117.
- Brynjolfsson, E., & McAfee, A. (2014): The Second Machine Age. Work, Progress, and Prosperity in a Time of Brilliant Technologies. New York: W.W. Norton & Company.
- Chen, H., Chiang, R.H.L., & Storey, V.C. (2012): “Business intelligence and analytics: From big data to big impact.” MIS Quarterly, 36 (4), 1165–1188.
- Forman, C., King, J.L., & Lyytinen, K. (2014): “Special Section Introduction – Information, Technology, and the Changing Nature of Work.” Information Systems Research, 25 (4), 789–795.
- Grint, K., & Woolgar, S. (2013): The Machine at Work. Technology, Work and Organization. Hoboken, NJ: John Wiley & Sons.
- Hanseth, O., Jacucci, E., Grisot, M., & Aanestad, M. (2006): “Reflexive Standardization: Side Effects and Complexity in Standard Making.” The Mississippi Quarterly, 30, 563–581.
- Leonardi, P.M., & Bailey, D.E. (2008): “Transformational Technologies and the Creation of New Work Practices: Making Implicit Knowledge Explicit in Task-Based Offshoring.” MIS Quarterly, 32 (2), 411–436.
- McCorduck, P., Minsky, M., Selfridge, O.G., & Simon, H.A. (1977): “History of Artificial Intelligence.” In: IJCAI ‘77 Proceedings of the 5th International Joint Conference on Artificial Intelligence, Cambridge, USA, August 22–25, 1977. San Francisco: Morgan Kaufmann Publishers, 951–954.
- Monteiro, E., & Hanseth, O. (1996): “Social Shaping of Information Infrastructure: On Being Specific about the Technology.” In: W.J. Orlikowski (ed.): Information Technology and Changes in Organizational Work. London: Chapman and Hall, 325–343.
- Orlikowski, W.J. (1992): “The Duality of Technology: Rethinking the Concept of Technology in Organizations.” Organization Science, 3 (3), 398–427.
- Osterlund, C., & Carlile, P. (2005): “Relations in practice: sorting through practice theories on knowledge sharing in complex organizations.” Information Society, 21 (2), 91–107.
- Prevedello, L.M., Erdal, B.S., Ryu, J.L., Little, K.J., Demirer, M., Qian, S., & White, R.D. (2017): “Automated Critical Test Findings Identification and Online Notification System Using Artificial Intelligence in Imaging.” Radiology, 285 (3), 923–931.
- Smith, M.J., & Carayon, P. (1995): “New technology, automation, and work organization: Stress problems and improved technology implementation strategies.” International Journal of Human Factors in Manufacturing, 5 (1), 99–116.
- Trist, E.L., & Bamforth, K.W. (1951): “Some social and psychological consequences of the Longwall Method of coal-getting: An examination of the psychological situation and defences of a work group in relation to the social structure and technological content of the work system.” Human Relations, 4 (1), 3–38.
- Yoo, Y., Boland, R.J., Lyytinen, K., & Majchrzak, A. (2012): “Organizing for Innovation in the Digitized World.” Organization Science, 23 (5), 1398–1408.