-------- Forwarded Message -------- Subject: [AISWorld] Call for Papers: Special Issue of Information Systems Research Humans, Algorithms, and Augmented Intelligence: The Future of Work, Organizations and Society Date: Thu, 19 Jul 2018 21:05:43 +0000 From: Jain, Hemant hemant-jain@utc.edu To: aisworld@lists.aisnet.org aisworld@lists.aisnet.org
Call for Papers: Special Issue of Information Systems Research Humans, Algorithms, and Augmented Intelligence: The Future of Work, Organizations and Society
Guest Editors: Hemant Jain, University of Tennessee Chattanooga Balaji Padmanabhan, University of South Florida Paul A. Pavlou, Temple University Raghu T Santanam, Arizona State University
“…the main intellectual advances will be made by men and computers working together in intimate association” -- J C R Licklider, 1960.
While artificial intelligence, machine learning, and other autonomic technologies are usually in the spotlight, many important problems are often solved through human beings and computers working cooperatively. The design of information systems has to focus as much on Intelligence Augmentation (IA), defined as computers enhancing human intelligence, as it would on Artificial Intelligence (AI), commonly defined as computers replacing human beings. Additionally, recent concerns about AI raised by pioneers like Stephen Hawking, Bill Gates, and Elon Musk raises major issues related to control in system design.1 IA requires a focus on design that optimally combines the abilities of human beings with various AI technologies and algorithms while keeping the ultimate control of human beings. As such, designers of information systems have to increase their focus on interactions, control, and interface points such that the resulting system is efficient, effective and addresses the issues of appropriate human control. Applications of IA are beginning to emerge in a number of domains such as cybersecurity, counter-terrorism, healthcare, and space exploration, among others. There are also several applications to the design of information systems. This Special Issue of Information Systems Research invites researchers to submit their best work to highlight how they are beginning to seamlessly integrate human and computer intelligence to solve interesting and important problems that impact the future of work, organizations, and broadly society.
BACKGROUND In the 1960’s, Engelbart and Licklider (both managed research programs at DARPA) pioneered the arguments for “human-computer symbiosis” (Licklider 1960). A fundamental assumption behind the need for human-computer symbiosis is that computers and human brains have different problem-solving capabilities. As such, IA research pursues design ideas that are intended to optimize the combined computational potential of human beings and computers. One branch of IA very familiar to Information Systems researchers is Human Computer Interaction (HCI). One of the pioneers of the HCI approach, Terry Winograd, has commented on the tensions between the AI and the HCI camps, and the associated “rationalistic” and “design” perspectives that they represent (Winograd 2006). Some parts of AI attempted to model human beings as cognitive machines and sought to build human-like AI systems. HCI, on the other hand, focused on a design approach which emphasizes interpretation, human behavior, and experimentation. Winograd quotes David Kelley, the renowned design thinker, as saying: “Enlightened trial and error outperforms the planning of flawless intellect”, suggesting the importance of iteratively improving by modeling the interaction between humans and AI. However, HCI is not the only perspective to human-computer symbiosis. Large scale computational problems often cannot be solved by either computer or humans alone – such problems are termed “human computation problems” (von Ahn 2008). For instance, crowd-sourcing strategies for many messy large-scale image or character recognition problems fall into this domain. Human computation problems rely on harnessing human processing power (i.e., common sense) to solve problems that computers are not yet good at solving. More interestingly, many early human computation problem-solving approaches have utilized gamification strategies that seem to be very well aligned with the HCI tradition of “design approach.” Given the increasing role AI plays in society today, the White House issued an RFI in 2016 to solicit commentaries and feedback on the role of AI for current and future needs of the economy. A report summarizing the responses to the RFI was released recently by the White House. IBM’s response to the RFI declared an emphasis on Augmented Intelligence in IBM’s approach to AI – “We call our particular approach to augmented intelligence “cognitive computing.” Cognitive computing is a comprehensive set of capabilities based on technologies such as machine learning, reasoning and decision technologies; language, speech and vision technologies; human interface technologies; distributed and high-performance computing; and new computing architectures and devices. When purposefully integrated, these capabilities are designed to solve a wide range of practical problems, boost productivity, and foster new discoveries across many industries.” In contrast, Google’s approach to AI, especially its search engine design, is also arguably more in the tradition of IA than AI. SPECIAL ISSUE FOCUS Recent developments in hardware, sensor and networking technologies combined with significant growth in Internet of Things (IOT) devices has increased interested in combining them with AI technologies to develop completely autonomous systems, such as driverless cars. The design of these systems poses unique technical, organizational, societal, and ethical questions. The human-computer symbiosis has potential to address some of these difficult issues. IS researchers (including many authors in ISR) have embraced both AI and IA traditions. Recent publications in ISR have revived both the design and rational schools of thoughts in research papers, notes and commentaries (see for example, Gregory and Muntermann (2014); Dhar et al., (2014); Clarke et al., (2016); and Meyer et al., (2014)). However, there is still a lack of coherent discussion and an integrated body of literature on the direct implications of how IA and AI research can contribute to organizational and societal applications and to their impact on the future of work. This Special Issue of Information Systems Research is intended to begin a new dialog on the potential synergies between IA and AI within the context of IS research. Given the long tradition of IS researchers to cross-disciplinary boundaries, we are confident of attracting a large number of high-quality submissions that will highlight the prevailing knowledge and research endeavors in the discipline and beyond. We hope to showcase the best research in this domain as part of this Special Issue. Topics of interest include but are not limited to:
* Design approaches for effectively combining human and computer cognitive power. * Applications and evaluation of human-computer symbiosis in various industry sectors, including healthcare, education, finance, cybersecurity, and transportation, among others. * Generalizable modeling innovations and applications that bridge IA and AI concepts. * Evaluation of theoretical predictions on how human beings and computers collaborate in solving large-scale computational problems. * Social, behavioral, and economic implications of AI and IA, including how they may impact the nature and future of work, productivity, jobs, and industries. * Theoretical predictions and evaluations of legal, policy, governance and business models associated with applications of AI and IA systems in various industries and markets. * Issues related to human control in the design of IA systems.
[1] http://observer.com/2015/08/stephen-hawking-elon-musk-and-bill-gates-warn-ab... Accessed 2/3/2017
2 https://obamawhitehouse.archives.gov/sites/whitehouse.gov/files/documents/Ar... Accessed 2/2/2017
[1]3 https://www.research.ibm.com/cognitive-computing/ostp/rfi-response.shtml Accessed 2/2/2017
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TIMELINE Full Papers Due: December 1, 2018 Initial Screening Decisions: January 1, 2019 Round 1 Decisions: April 15, 2019 Workshop: June 15-16, 2019 (tentative) 1st Round Revisions Due: October 15, 2019 Round 2 Decisions: January 15, 2020
EDITORIAL BOARD
Ohad Barzilay, Tel Aviv University Gordon Burtch, University of Minnesota Ram Chellappa, Emory University Theodoros Evgenious, INSEAD Tomer Geva, Tel Aviv University Alan Hevner, University of South Florida Kevin (Yili) Hong, Arizona State University Panos Ipeirotis, New York University Nishtha Langer, Rensselaer Polytechnic Institute Ting Li, Rotterdam School of Management Xitong Li, HEC Paris Jiahui Mo, Nanyang Technological University Joe Nandakumar, University of Warwick Gautam Pant, University of Iowa Sandeep Purao, Bentley University Liangfei Qiu, University of Florida Sam Ransbotham, Boston College Benjamin Shao, Arizona State University Atish Sinha, University of Wisconsin Milwaukee Tianshu Sun, University of Southern California Anjana Susarla, Michigan State University Prasanna Tambe, University of Pennsylvania Monica Tremblay, College of William and Mary Sunil Wattal, Temple University Heng Xu, Pennsylvania State University Jingjing Zhang, Indiana University Rong Zheng, Hong Kong University of Science and Technology Leon Zhao, City University of Hong Kong Hangjung Zo, KAIST
References
Clarke, R., Burton-Jones, A., & Weber, R. (2016). On the Ontological Quality and Logical Quality of Conceptual-Modeling Grammars: The Need for a Dual Perspective. Information Systems Research, 27(2), 365-382.
Dhar, V., Geva, T., Oestreicher-Singer, G., & Sundararajan, A. (2014). Prediction in economic networks. Information Systems Research, 25(2), 264-284.
Gregory, R. W., & Muntermann, J. (2014). Research Note—Heuristic Theorizing: Proactively Generating Design Theories. Information Systems Research, 25(3), 639-653.
Licklider, J. C. (1960). Man-computer symbiosis. IRE transactions on human factors in electronics, (1), 4-11. Meyer, G., Adomavicius, G., Johnson, P. E., Elidrisi, M., Rush, W. A., Sperl-Hillen, J. M., & O'Connor, P. J. (2014). A machine learning approach to improving dynamic decision making. Information Systems Research, 25(2), 239-263.
Von Ahn, L., & Dabbish, L. (2008). Designing games with a purpose. Communications of the ACM, 51(8), 58-67.
Winograd, T. (2006). Shifting viewpoints: Artificial intelligence and human–computer interaction. Artificial Intelligence, 170(18), 1256-1258.
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