Visual Apollo: A Graphical Exploration of Computer-Human Relationships

Abstract

Introduction As NASA struggles with an uncertain national policy environment, it is seeking new combinations of human and robotic modes of exploration. During the Apollo era, establishing human presence in space was an integral part of NASA’s work.1 More recently, the Mars Exploration Rover missions have demonstrated how rich and successful remote exploration of a planetary surface can be.2 Space exploration is one of many examples of technical operations conducted in extreme environments that are raising new questions about the relative importance of human and remote presence. What does it mean to “be there?” Researchers, designers, and operators of situated robotics and remote technologies are testing increasingly distributed configurations of human–machine teams in technical operations, from space exploration to surgery.3 However, with new forms of automation come unexpected changes in the social organization of work. If we are to understand the implications of increased automation for control, responsibility, and safety, we need to look beyond the methodological boundaries of traditional computer–human interaction studies in both human factors and social studies of technology. Human factors studies tend to focus on individual operators and quantitative representations.4 They emphasize workload, interface, and situational awareness but frequently overlook the social organization of human–machine teams and the cultural production of operator roles. Yet these factors can have profound effects on the acceptance of new technologies, in both engineering decisions and national policy making. Although social studies of technology address these broader socio-cultural issues, they often do so in formats that privilege qualitative data, incremental analysis, and linear explanations at the expense of considering technical and temporal measures of events.5 The study of distributed computer–human relationships requires new methods that are capable of picking up on multi-channel interactions. In our collaborative work, we are developing methods that bring together a combination of individual, social, quantitative, and qualitative data in rich, graphical, real-time representations.6 1 For more on the changing role of humans in spaceflight, see David Mindell, Digital Apollo: Human and Machine in Spaceflight (Cambridge: MIT Press, 2008). 2 See William Clancey, Working on Mars: Voyages of Scientific Discovery with the Mars Exploration Rovers (Cambridge: MIT Press, 2012); William Clancey, “Becoming a Rover,” in Simulation and Its Discontents, Sherry Turkle, ed. (Cambridge: MIT Press, 2008), 107–27; and Zara Mirmalek, “Solar Discrepancies: Mars Exploration and the Curious Problem of Interplanetary Time” (doctoral thesis, University of California, San Diego, 2008). 3 James Hollan, Edwin Hutchins, and David Kirsh, “Distributed Cognition: Toward a New Foundation for Human–Computer Interaction Research,” ACM Transactions on Computer–Human Interaction 7, no. 2 (June 2000): 174–96. 4 Raja Parasuraman, “A Model for Types and Levels of Human Interaction with Automation,” IEEE Transactions on Systems, Man, and Cybernetics 30, no. 3 (2000): 286–97; Thomas B. Sheridan, Humans and Automation: System Design and Research Issues (New York: WileyInterscience, 2002). 5 Bruno Latour, Science in Action: How to Follow Scientists and Engineers Through Society (Cambridge: Harvard University Press, 1988); Gary Downey and Joseph Dumit, Cyborgs & Citadels: Anthropological Interventions in Emerging Sciences and Technologies (Santa Fe, NM: School of American Research Press, 1997); Lucille Suchman, Human–Machine Reconfigurations: Plans and Situated Actions (New York: Cambridge University Press, 2007).