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. <br>_______________________________________________ <br>EASST's Eurograd mailing list <br>Eurograd (at) lists.easst.net <br>Unsubscribe or edit subscription options: http://lists.easst.net/listinfo.cgi/eurograd-easst.net <br> <br>Meet us via https://twitter.com/STSeasst <br> <br>Report abuses of this list to Eurograd-owner@lists.easst.netview formatted text
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