Message posted on 24/11/2018

CFP EGOS 2019: Work in the Age of Intelligence Subtheme

                Sub-theme 35: Work in the Age of Intelligence: Augmentation, Agency and
<br>Infrastructure
<br>Convenors:
<br>Ingrid Erickson
<br>Syracuse University, USA
<br>imericks@syr.edu 
<br>Margunn Aanestad
<br>University of Oslo, Norway
<br>margunn@ifi.uio.no 
<br>Carsten Østerlund
<br>Syracuse University, USA
<br>costerlu@syr.edu 
<br>Call for Papers
<br>
<br>The relationship of work to technology has long been studied (e.g., Barley,
<br>1986; Orlikowski, 1992; Trist & Bamforth, 1951), from the roboticization of
<br>factory lines (e.g., Argote et al., 1983; Grint & Woolgar, 2013; Smith &
<br>Carayon, 1995) to the integration of information and computing technology into
<br>knowledge work (e.g., Hanseth et al., 2006; Leonardi & Bailey, 2008; Osterlund
<br>& Carlile, 2005). As more and more digital technology becomes elemental to
<br>modern forms of work, it is sometimes difficult to separate tasks from tools,
<br>procedures from platforms. Today, not only is work primarily digital and
<br>computational, but it is fast becoming algorithmic with the introduction of
<br>artificial intelligence into existing procedures and practices (Brynjolfsson &
<br>McAfee, 2014). For instance, radiologists can now leverage artificial
<br>intelligence to analyze patients’ scans instead of relying on their trained
<br>eyes alone; these machines, using intelligent algorithms, are reported to have
<br>a higher rate of tumor recognition than even the most well-trained experts
<br>(Aerts, 2017; Prevedello et al., 2017).
<br>
<br>Noting that there are more and more instances of organizations utilizing
<br>artificial intelligence for strategic and operational ends, this sub-theme
<br>seeks to better understand these relationships by drawing in empirical
<br>scholarship that studies work at this particular human-technology frontier.
<br>Incumbent in our desire to convene this conversation are three driving
<br>questions:
<br>
<br>Where and how is artificial intelligence being used in contemporary
<br>organizations?
<br>How do these examples help us understand shifts in work practices (i.e., are
<br>artificial agents new collaborators, embedded technical constraints, something
<br>else entirely)?
<br>How can enquiries into to working with smart agents reveal what is
<br>intrinsically human about modern forms of work?
<br>
<br>Artificial intelligence (AI) is a current buzzword in business, but it is a
<br>technology that has a long history (McCorduck et al., 1977). In some ways a
<br>simple calculator displays ‘intelligence’ in its seemingly cognitive
<br>ability to calculate sums rapidly. Yet, today’s reference to the term tends
<br>to connote the predictive, rather than the mere processing, power of
<br>computation (Chen et al., 2012). Of course, prediction is still a function of
<br>processing, but more importantly it is also derivative of the analysis of
<br>great stores of past data. These digital traces of the past, when run through
<br>powerful machines, reveal patterns. It is these patterns that make up the
<br>ingredients of algorithms, which are essentially recipes linking past patterns
<br>to potential future patterns. AI occurs in our daily lives everyday when, for
<br>example, Amazon recommends books that you might like based on a current
<br>selection. Scale this up a bit and you have the example of an autonomous
<br>vehicle – a machine that is able to not only see links between Item A and B,
<br>but to string a multitude of these relations together and act on them in real
<br>time, essentially simulating a human driver who can navigate a complex
<br>terrain. The sophistication of the ‘intelligence’ of an autonomous vehicle
<br>extends beyond a simple recommendation; instead, it is a result of both
<br>predictive power and also machine learning, a computational process whereby a
<br>computer learns from environmental feedback. As this feedback comes in, the
<br>machine ‘learns’ and gradually improves its operations, ad infinitum.
<br>
<br>The intersection of work and artificial intelligence is occurring along a
<br>complex spectrum, ranging from things such as the increased use of recommender
<br>systems in decision sequences (as hinted at in the Amazon example above) to
<br>the incorporation of fully fledged intelligent machines, as in the case of
<br>autonomous vehicles upending the jobs of truck drivers or robots conducting
<br>surgery. Of course, these variations mirror the wide diversity of work tasks
<br>today, but they also reflect the information infrastructures (Bowker et al.,
<br>2009; Monteiro & Hanseth, 1996) in which the AI is embedded. While it is
<br>conceptually powerful to think of the direct relationship between artificial
<br>intelligence and work, rarely do they come together without a mediator. These
<br>intermediaries provide platforms for necessary activities to run, they help to
<br>integrate disparate technologies with one another, and, when functioning
<br>properly, they fade into the background and become embedded in the norms and
<br>rules that govern an organization or a culture. To a financial analyst, the
<br>practice of utilizing AI may occur within the use of predictive analytics
<br>package on a organizationally-mandated data platform – perhaps one that
<br>optimizes a complex set of portfolios by visualizing them in such a way that a
<br>quick decision can be rendered easily. A truck driver, on the other hand, has
<br>quite a different experience of AI. Not only is he or she enveloped by AI in
<br>material form, but experientially these drivers are likely limited to a narrow
<br>set of options well before the engine is even turned on. Is the driver then an
<br>agent of the machine and the analyst a collaborator? These are not only
<br>questions of task design, perceived efficiency, and financial optimization but
<br>also of a worker’s agency and the boundaries in which they are intended (or
<br>allowed) to act.
<br>
<br>In recent years information infrastructures have become more widely studied,
<br>with a particular interest in the ways that their inherent digital
<br>extensibility supports generativity and innovation (e.g., Forman et al., 2014;
<br>Yoo et al., 2012). Less well studied, however, is the way that information
<br>infrastructures encode certain practices because of their reliance on
<br>algorithms and artificial intelligence. We see this emphasis in our proposed
<br>sub-theme as a way to take up the mantle of prior work on infrastructures, but
<br>also to provide a forum, in line with the general theme of the annual
<br>convening, to consider how AI may be challenging (or enlightening)
<br>organizations via the increased reliance on and organization of work via
<br>information infrastructures.
<br>
<br>We encourage submissions that address the broad subject of automation and work
<br>from an equally broad array of disciplinary scholars. We invite papers that
<br>deal with (but are not limited to) the following topic areas:
<br>AI in the collective
<br>AI knowledge work
<br>AI now and then
<br>Algorithmic infrastructures
<br>Algorithmic phenomena in the organization of work
<br>Breakdowns in AI and work
<br>Designing AI-Human practices
<br>Dynamic relationships between AI and humans
<br>Methodological implication of algorithmic phenomena
<br>Nature of coordination and collaboration in the age of the “smart
<br>machine”
<br>Predictions in practice
<br>Roboticization and hybrid agency
<br>Sociomaterial theorizing about new forms of work
<br>
<br>Short papers should focus on the main ideas of the paper, i.e. they should
<br>explain the purpose of the paper, theoretical background, the research gap
<br>that is addressed, the approach taken, the methods of analysis (in empirical
<br>papers), main findings, and contributions. In addition, it is useful to
<br>indicate clearly how the paper links with the sub-theme and the overall theme
<br>of the Colloquium, although not all papers need to focus on the overall theme.
<br>Creativity, innovativeness, theoretical grounding, and critical thinking are
<br>typical characteristics of EGOS papers.
<br>Your short paper should comprise 3,000 words (incl. references, all appendices
<br>and other material).
<br>Due:  Monday, January 14, 2019, 23:59:59 CET [Central European Time]
<br>
<br>References
<br>Aerts, H.J.W.L. (2017): “Data Science in Radiology: A Path Forward.”
<br>Clinical Cancer Research, 24 (3), 532–534.
<br>Argote, L., Goodman, P.S., & Schkade, D. (1983): “The Human Side of
<br>Robotics: How Workers React to a Robot.” Sloan Management Review, 24 (3),
<br>31–41.
<br>Barley, S.R. (1986): “Technology as an Occasion for Structuring: Evidence
<br>from Observations of CT Scanners and the Social Order of Radiology
<br>Departments.” Administrative Science Quarterly, 31 (1), 78–108.
<br>Bowker, G.C., Baker, K., Millerand, F., & Ribes, D. (2009): “Toward
<br>Information Infrastructure Studies: Ways of Knowing in a Networked
<br>Environment.” In: J. Hunsinger, L. Klastrup & M. Allen (eds.): International
<br>Handbook of Internet Research. Dordrecht: Springer, 97–117.
<br>Brynjolfsson, E., & McAfee, A. (2014): The Second Machine Age. Work, Progress,
<br>and Prosperity in a Time of Brilliant Technologies. New York: W.W. Norton &
<br>Company.
<br>Chen, H., Chiang, R.H.L., & Storey, V.C. (2012): “Business intelligence and
<br>analytics: From big data to big impact.” MIS Quarterly, 36 (4),
<br>1165–1188.
<br>Forman, C., King, J.L., & Lyytinen, K. (2014): “Special Section Introduction
<br>– Information, Technology, and the Changing Nature of Work.” Information
<br>Systems Research, 25 (4), 789–795.
<br>Grint, K., & Woolgar, S. (2013): The Machine at Work. Technology, Work and
<br>Organization. Hoboken, NJ: John Wiley & Sons.
<br>Hanseth, O., Jacucci, E., Grisot, M., & Aanestad, M. (2006): “Reflexive
<br>Standardization: Side Effects and Complexity in Standard Making.” The
<br>Mississippi Quarterly, 30, 563–581.
<br>Leonardi, P.M., & Bailey, D.E. (2008): “Transformational Technologies and
<br>the Creation of New Work Practices: Making Implicit Knowledge Explicit in
<br>Task-Based Offshoring.” MIS Quarterly, 32 (2), 411–436.
<br>McCorduck, P., Minsky, M., Selfridge, O.G., & Simon, H.A. (1977): “History
<br>of Artificial Intelligence.” In: IJCAI ‘77 Proceedings of the 5th
<br>International Joint Conference on Artificial Intelligence, Cambridge, USA,
<br>August 22–25, 1977. San Francisco: Morgan Kaufmann Publishers, 951–954.
<br>Monteiro, E., & Hanseth, O. (1996): “Social Shaping of Information
<br>Infrastructure: On Being Specific about the Technology.” In: W.J. Orlikowski
<br>(ed.): Information Technology and Changes in Organizational Work. London:
<br>Chapman and Hall, 325–343.
<br>Orlikowski, W.J. (1992): “The Duality of Technology: Rethinking the Concept
<br>of Technology in Organizations.” Organization Science, 3 (3), 398–427.
<br>Osterlund, C., & Carlile, P. (2005): “Relations in practice: sorting through
<br>practice theories on knowledge sharing in complex organizations.”
<br>Information Society, 21 (2), 91–107.
<br>Prevedello, L.M., Erdal, B.S., Ryu, J.L., Little, K.J., Demirer, M., Qian, S.,
<br>& White, R.D. (2017): “Automated Critical Test Findings Identification and
<br>Online Notification System Using Artificial Intelligence in Imaging.”
<br>Radiology, 285 (3), 923–931.
<br>Smith, M.J., & Carayon, P. (1995): “New technology, automation, and work
<br>organization: Stress problems and improved technology implementation
<br>strategies.” International Journal of Human Factors in Manufacturing, 5 (1),
<br>99–116.
<br>Trist, E.L., & Bamforth, K.W. (1951): “Some social and psychological
<br>consequences of the Longwall Method of coal-getting: An examination of the
<br>psychological situation and defences of a work group in relation to the social
<br>structure and technological content of the work system.” Human Relations, 4
<br>(1), 3–38.
<br>Yoo, Y., Boland, R.J., Lyytinen, K., & Majchrzak, A. (2012): “Organizing for
<br>Innovation in the Digitized World.” Organization Science, 23 (5),
<br>1398–1408.
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