@proceedings {petridis2023anglekindling, title = {AngleKindling: Supporting Journalistic Angle Ideation with Large Language Models}, year = {2023}, doi = {10.1145/3544548.3580907}, url = {https://savvaspetridis.github.io/papers/anglekindling.pdf}, author = {Petridis, Savvas and Diakopoulos, Nicholas and Crowston, Kevin and Hansen, Mark and Henderson, Keren and Jastrzebski, Stan and Nickerson, Jefrey V and Chilton, Lydia B} } @article {9998, title = {Artificial intelligence in information systems: State of the art and research roadmap}, journal = {Communications of the Association for Information Systems (CAIS)}, volume = {50}, year = {2022}, abstract = {

Many would argue that artificial intelligence (AI) is not just a technology but represents a paradigmatic shift in the relationship between humans and machines. Much of the literature assumes that AI-powered practices are substantially different and profoundly changes organizational structures, communication, affordances, and ecosystems. However, research in AI is often fragmented and lacks clarity. While the Information Systems (IS) field can play a pivotal role in the emergence and use of AI, there is a need for a clear direction that specifies how IS can contribute and what are to be the key research themes and questions. This paper draws on a PDW at ICIS 2020 and the discussions that followed. It summarizes and synthesizes five decades of the impact of AI on organizational practices, providing views from various perspectives. It identifies weaknesses in the current AI literature as measured against conceptual clarity, theoretical glue, cumulative tradition, parsimony, and applicability. The paper concludes by identifying direct actions that the IS research community can undertake to address these issues. The final contribution is a next-step research agenda to guide AI research in the coming years.

}, doi = {10.17705/1CAIS.05017}, attachments = {https://waim.network/sites/crowston.syr.edu/files/Artificial\%20Intelligence\%20in\%20Information\%20Systems\%20State\%20of\%20the\%20Art.pdf}, author = {P{\"a}r J. {\r A}gerfalk and Kieran Conboy and Kevin Crowston and Sirkka L. Jarvenpaa and Jenny Eriksson Lundstr{\"o}m and Patrick Mikalef and Sudha Ram} } @article {Sidaoui2020, title = {AI feel you: customer experience assessment via chatbot interviews}, journal = {Journal of Service Management}, year = {2020}, abstract = {Purpose: While customer experience (CE) is recognized as a critical determinant of business success, both academics and managers are yet to find a means to gain a comprehensive understanding of CE cost-effectively. The authors argue that the application of relevant AI technology could help address this challenge. Employing interactively prompted narrative storytelling, and the authors investigate the effectiveness of sentiment analysis (SA) on extracting valuable CE insights from primary qualitative data generated via chatbot interviews. Design/methodology/approach: Drawing on a granular and semantically clear framework for studying CE feelings, an artificial intelligence (AI) augmented chatbot was designed. The chatbot interviewed a crowdsourced sample of consumers about their recalled service experience feelings. By combining free-text and closed-ended questions, the authors were able to compare extracted sentiment polarities against established measurement scales and empirically validate our novel approach. Findings: The authors demonstrate that SA can effectively extract CE feelings from primary chatbot data. This findings also suggest that further enhancement in accuracy can be achieved via improvements in the interplay between the chatbot interviewer and SA extraction algorithms. Research limitations/implications: The proposed customer-centric approach can help service companies to study and better understand CE feelings in a cost-effective and scalable manner. The AI-augmented chatbots can also help companies to foster immersive and engaging relationships with customers. This study focuses on feelings, warranting further research on AI{\textquoteright}s value in studying other CE elements. Originality/value: The unique inquisitive role of AI-infused chatbots in conducting interviews and analyzing data in realtime, offers considerable potential for studying CE and other subjective constructs.}, keywords = {artificial intelligence, chatbot, Customer experience, Customer feelings, Sentiment analysis, Storytelling}, issn = {17575818}, doi = {10.1108/JOSM-11-2019-0341}, author = {Sidaoui, Karim and Jaakkola, Matti and Burton, Jamie} } @article {2020, title = {Artificial intelligence and the world of work, a co-constitutive relationship}, journal = {Journal of the Association for Information Science and Technology}, year = {2020}, abstract = {

The use of intelligent machines{\textemdash}digital technologies that feature data- driven forms of customization, learning, and autonomous action{\textemdash}is rapidly growing and will continue to impact many industries and domains. This is consequential for communities of researchers, educators, and practitioners concerned with studying, supporting, and educating information profes- sionals. In the face of new developments in artificial intelligence (AI), the research community faces 3 questions: (a) How is AI becoming part of the world of work? (b) How is the world of work becoming part of AI? and (c) How can the information community help address this topic of Work in the Age of Intelligent Machines (WAIM)? This opinion piece considers these 3 questions by drawing on discussion from an engaging 2019 iConference workshop organized by the NSF supported WAIM research coordination net- work (note: https://waim.network).

}, issn = {2330-1635}, doi = {10.1002/asi.24388}, author = {{\O}sterlund, Carsten and Jarrahi, Mohammad Hossein and Willis, Matthew and Boyd, Karen and Wolf, Christine} } @booklet {Jha2020, title = {Automation and Radiology{\textemdash}Part 1}, howpublished = {Academic Radiology}, volume = {27}, number = {1}, year = {2020}, pages = {147{\textendash}149}, publisher = {Elsevier Inc.}, issn = {18784046}, doi = {10.1016/j.acra.2019.10.026}, url = {https://doi.org/10.1016/j.acra.2019.10.026}, author = {Jha, Saurabh} } @article {Johnson2020, title = {The benefits of eHRM and AI for talent acquisition}, journal = {Journal of Tourism Futures}, year = {2020}, abstract = {Purpose : The hospitality and tourism industry faces a number of workforce challenges, especially the high turnover rates and associated replacement costs associated with continually identifying and hiring new employees. The purpose of this paper is to discuss how hospitality and tourism organizations can use electronic human resource management (eHRM) and artificial intelligence (AI) to help recruit and select qualified employees, increase individual retention rates and decrease the time needed to replace employees. Specifically, it discusses how e-recruiting and e-selection and AI tools can help hospitality and tourism organizations improve recruiting and selection outcomes. Design/methodology/approach: Research on eHRM, AI, employee recruitment and employee selection are applied to the hospitality and tourism industry and insights for how eHRM and AI can be applied to the industry are discussed. Findings: eHRM and AI have the potential to transform how the hospitality and tourism industry recruit and select employees. However, care must be taken to ensure that the insights gained and the decisions made are well received by employees and lead to better employee and organizational outcomes. Research limitations/implications: This paper represents the first research that integrates research from eHRM and AI and applies it to the hospitality and tourism industry. Originality/value: This paper represents the first research that integrates research from eHRM and AI and applies it to the hospitality and tourism industry.}, keywords = {artificial intelligence, e-HRM, e-recruiting, e-selection, eHRM, Electronic human resource management, Employee selection, Recruitment, Selection}, issn = {2055592X}, doi = {10.1108/JTF-02-2020-0013}, author = {Johnson, Richard D and Stone, Dianna L and Lukaszewski, Kimberly M} } @article {EganadelSol2020, title = {The Future of Work in Developing Economies}, journal = {MIT Sloan Management Review}, volume = {61}, number = {2}, year = {2020}, pages = {1{\textendash}3}, abstract = {

Much has been written about the rise of automation in developed countries. Economists have been busily creating models seeking to quantify the likely impact of automation on employment. However, far less has been written about the potential effects on work in developing nations. This is surprising, given that automation may be especially troublesome for developing economies. Here, del Sol and Joyce examine the effects of large-scale automation on workers in developing countries.

}, keywords = {Armenia, Asia, Austria, automation, Bolivia, Business And Economics{\textendash}Management, China, Developing countries{\textendash}LDCs, Employment, future, Georgia (country), Ghana, Impact analysis, Kenya, Kuala Lumpur Malaysia, Laos, Republic of North Macedonia, South Korea, Sri Lanka, United States{\textendash}US, Vietnam, Workers}, issn = {15329194}, author = {Egana del Sol, Pablo and Joyce, Connor and Del Sol, Pablo Ega{\~n}a and Joyce, Connor} } @proceedings {2018, title = {The Genie in the Bottle: Different Stakeholders, Different Interpretations of Machine Learning}, year = {2020}, type = {Working paper}, address = {Wailea, HI}, abstract = {

Machine learning (ML) constitute an algorithmic phenomenon with some distinctive characteristics (e.g., being trained, probabilistic). Our understanding of such systems is limited when it comes to how these unique characteristics play out in organizational settings and what challenges different groups of users will face in working with them. We explore how people developing or using an ML system come to understand its capabilities and challenges. We draw on the social construction of technology tradition to frame our analysis of interviews and discussion board posts involving designers and users of a ML-supported citizen-science crowdsourcing project named Gravity Spy. Our findings reveal some of the challenges facing different relevant social groups. We find that groups with less interaction with the technology have their understanding. We find that the type of understandings achieved by groups having less interaction with the technology is shaped by outside influences rather than the specifics of the system and its role in the project. Notable, some users mistake human input for ML input. This initial understanding of how different participants understand and engage with ML point to challenges that need to be overcome to help participants deal with the opaque position ML often hold in a work system.

}, doi = {10.24251/HICSS.2020.719 }, attachments = {https://waim.network/sites/crowston.syr.edu/files/Social_Construction_of_ML_in_GS_HICCS2020.pdf}, author = {Mahboobeh Harandi and Kevin Crowston and Corey Jackson and Carsten {\O}sterlund} } @article {Jung2020, title = {Industrial robots, employment growth, and labor cost: A simultaneous equation analysis}, journal = {Technological Forecasting and Social Change}, volume = {159}, number = {June}, year = {2020}, pages = {120202}, publisher = {Elsevier}, abstract = {In recent years, the global rapid expansion of industrial robots has induced ever-increasing concerns for the cause and effect of such growth, particularly with regard to its relationship with labor. This paper analyzes the factors underlying the adoption of industrial robots, employment growth and structure, and labor costs, taking into account the two-way causalities between these variables. For the empirical analysis, we use the three-stage least squares (3SLS) method for the system of simultaneous equations and apply it to the panel data constructed for 42 countries. Explanatory variables for each equation include the dependent variables of other equations and exogenous variables, such as the labor market environment, physical and human capital, and country-specific social environment. The empirical results of the present study indicate that the increase in both unit labor costs and hourly compensation level induces an extensive application of industrial robots. Subsequently, the expansion of industrial robots leads to a reduction of unit labor costs; however, the hourly compensation level increases, implying that the productivity-enhancing effect exceeds the wage-increasing effect of industrial robots. The extensive use of industrial robots tends to suppress employment growth, confirming the labor-substituting effect of industrial robots; the observed trend disproportionately affects low-skilled labor.}, keywords = {Compensation level, Employment growth, Industrial robots, Labor cost, Simultaneous equation analysis}, issn = {00401625}, doi = {10.1016/j.techfore.2020.120202}, url = {https://doi.org/10.1016/j.techfore.2020.120202}, author = {Jung, Jin Hwa and Lim, Dong Geon} } @article {Brundage2020, title = {Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims}, year = {2020}, pages = {1{\textendash}9}, abstract = {With the recent wave of progress in artificial intelligence (AI) has come a growing awareness of the large-scale impacts of AI systems, and recognition that existing regulations and norms in industry and academia are insufficient to ensure responsible AI development. In order for AI developers to earn trust from system users, customers, civil society, governments, and other stakeholders that they are building AI responsibly, they will need to make verifiable claims to which they can be held accountable. Those outside of a given organization also need effective means of scrutinizing such claims. This report suggests various steps that different stakeholders can take to improve the verifiability of claims made about AI systems and their associated development processes, with a focus on providing evidence about the safety, security, fairness, and privacy protection of AI systems. We analyze ten mechanisms for this purpose{\textendash}spanning institutions, software, and hardware{\textendash}and make recommendations aimed at implementing, exploring, or improving those mechanisms.}, url = {http://arxiv.org/abs/2004.07213}, author = {Brundage, Miles and Avin, Shahar and Wang, Jasmine and Belfield, Haydn and Krueger, Gretchen and Hadfield, Gillian and Khlaaf, Heidy and Yang, Jingying and Toner, Helen and Fong, Ruth and Maharaj, Tegan and Koh, Pang Wei and Hooker, Sara and Leung, Jade and Trask, Andrew and Bluemke, Emma and Lebensold, Jonathan and O{\textquoteright}Keefe, Cullen and Koren, Mark and Ryffel, Th{\'e}o and Rubinovitz, JB and Besiroglu, Tamay and Carugati, Federica and Clark, Jack and Eckersley, Peter and de Haas, Sarah and Johnson, Maritza and Laurie, Ben and Ingerman, Alex and Krawczuk, Igor and Askell, Amanda and Cammarota, Rosario and Lohn, Andrew and Krueger, David and Stix, Charlotte and Henderson, Peter and Graham, Logan and Prunkl, Carina and Martin, Bianca and Seger, Elizabeth and Zilberman, Noa and H{\'E}igeartaigh, Se{\'a}n {\'O} and Kroeger, Frens and Sastry, Girish and Kagan, Rebecca and Weller, Adrian and Tse, Brian and Barnes, Elizabeth and Dafoe, Allan and Scharre, Paul and Herbert-Voss, Ariel and Rasser, Martijn and Sodhani, Shagun and Flynn, Carrick and Gilbert, Thomas Krendl and Dyer, Lisa and Khan, Saif and Bengio, Yoshua and Anderljung, Markus} } @article {2019, title = {Algorithms at War: The Promise, Peril, and Limits of Artificial Intelligence}, journal = {International Studies Review}, year = {2019}, month = {Dec-06-2020}, issn = {1521-9488}, doi = {10.1093/isr/viz025}, author = {Jensen, Benjamin M and Whyte, Christopher and Cuomo, Scott} } @article {2019, title = {Artificial intelligence in workplaces and how it will affect employment in Latvia}, journal = {Environment. Technology. Resources. Proceedings of the International Scientific and Practical Conference}, volume = {2}, year = {2019}, month = {Aug-06-2020}, pages = {154}, issn = {1691-5402}, doi = {10.17770/etr2019vol2.4151}, author = {{\v S}ukjurovs, Ilm{\={a}}rs and Zvirgzdina, Rosita and Jeromanova-Maura, Silva} } @article {2019, title = {Behavioural artificial intelligence: An agenda for systematic empirical studies of artificial inference}, journal = {AI \& SOCIETY}, year = {2019}, month = {Jun-12-2020}, issn = {0951-5666}, doi = {10.1007/s00146-019-00928-5}, author = {Pedersen, Tore and Johansen, Christian} } @article {2019, title = {A convolutional neural network trained with dermoscopic images performed on par with 145 dermatologists in a clinical melanoma image classification task}, journal = {European Journal of Cancer}, volume = {111}, year = {2019}, month = {Jan-04-2019}, pages = {148 - 154}, issn = {09598049}, doi = {10.1016/j.ejca.2019.02.005}, author = {Brinker, Titus J. and Hekler, Achim and Alexander Enk and Klode, Joachim and Hauschild, Axel and Berking, Carola and Schilling, Bastian and Haferkamp, Sebastian and Schadendorf, Dirk and Fr{\"o}hling, Stefan and Jochen Sven Utikal and von Kalle, Christof and Ludwig-Peitsch, Wiebke and Sirokay, Judith and Heinzerling, Lucie and Albrecht, Magarete and Baratella, Katharina and Bischof, Lena and Chorti, Eleftheria and Dith, Anna and Drusio, Christina and Giese, Nina and Gratsias, Emmanouil and Griewank, Klaus and Hallasch, Sandra and Hanhart, Zdenka and Herz, Saskia and Hohaus, Katja and Jansen, Philipp and Jockenh{\"o}fer, Finja and Kanaki, Theodora and Knispel, Sarah and Leonhard, Katja and Martaki, Anna and Matei, Liliana and Matull, Johanna and Olischewski, Alexandra and Petri, Maximilian and Placke, Jan-Malte and Raub, Simon and Salva, Katrin and Schlott, Swantje and Sody, Elsa and Steingrube, Nadine and Stoffels, Ingo and Ugurel, Selma and Sondermann, Wiebke and Zaremba, Anne and Gebhardt, Christoffer and Booken, Nina and Christolouka, Maria and Buder-Bakhaya, Kristina and Bokor-Billmann, Therezia and Alexander Enk and Gholam, Patrick and H{\"a}n{\ss}le, Holger and Salzmann, Martin and Sch{\"a}fer, Sarah and Sch{\"a}kel, Knut and Schank, Timo and Bohne, Ann-Sophie and Deffaa, Sophia and Drerup, Katharina and Egberts, Friederike and Erkens, Anna-Sophie and Ewald, Benjamin and Falkvoll, Sandra and Gerdes, Sascha and Harde, Viola and Hauschild, Axel and Jost, Marion and Kosova, Katja and Messinger, Laetitia and Metzner, Malte and Morrison, Kirsten and Motamedi, Rogina and Pinczker, Anja and Rosenthal, Anne and Scheller, Natalie and Schwarz, Thomas and St{\"o}lzl, Dora and Thielking, Federieke and Tomaschewski, Elena and Wehkamp, Ulrike and Weichenthal, Michael and Wiedow, Oliver and B{\"a}r, Claudia Maria and Bender-S{\"a}belkampf, Sophia and Horbr{\"u}gger, Marc and Karoglan, Ante and Kraas, Luise and Faulhaber, J{\"o}rg and Geraud, Cyrill and Guo, Ze and Koch, Philipp and Linke, Miriam and Maurier, Nolwenn and M{\"u}ller, Verena and Thomas, Benjamin and Jochen Sven Utikal and Alamri, Ali Saeed M. and Baczako, Andrea and Berking, Carola and Betke, Matthias and Haas, Carolin and Hartmann, Daniela and Heppt, Markus V. and Kilian, Katharina and Krammer, Sebastian and Lapczynski, Natalie Lidia and Mastnik, Sebastian and Nasifoglu, Suzan and Ruini, Cristel and Sattler, Elke and Schlaak, Max and Wolff, Hans and Achatz, Birgit and Bergbreiter, Astrid and Drexler, Konstantin and Ettinger, Monika and Haferkamp, Sebastian and Halupczok, Anna and Hegemann, Marie and Dinauer, Verena and Maagk, Maria and Mickler, Marion and Philipp, Biance and Wilm, Anna and Wittmann, Constanze and Gesierich, Anja and Glutsch, Valerie and Kahlert, Katrin and Kerstan, Andreas and Schilling, Bastian and Schr{\"u}fer, Philipp} } @article {2019, title = {Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task}, journal = {European Journal of Cancer}, volume = {113}, year = {2019}, month = {Jan-05-2019}, pages = {47 - 54}, issn = {09598049}, doi = {10.1016/j.ejca.2019.04.001}, author = {Brinker, Titus J. and Hekler, Achim and Alexander Enk and Klode, Joachim and Hauschild, Axel and Berking, Carola and Schilling, Bastian and Haferkamp, Sebastian and Schadendorf, Dirk and Holland-Letz, Tim and Jochen Sven Utikal and von Kalle, Christof and Ludwig-Peitsch, Wiebke and Sirokay, Judith and Heinzerling, Lucie and Albrecht, Magarete and Baratella, Katharina and Bischof, Lena and Chorti, Eleftheria and Dith, Anna and Drusio, Christina and Giese, Nina and Gratsias, Emmanouil and Griewank, Klaus and Hallasch, Sandra and Hanhart, Zdenka and Herz, Saskia and Hohaus, Katja and Jansen, Philipp and Jockenh{\"o}fer, Finja and Kanaki, Theodora and Knispel, Sarah and Leonhard, Katja and Martaki, Anna and Matei, Liliana and Matull, Johanna and Olischewski, Alexandra and Petri, Maximilian and Placke, Jan-Malte and Raub, Simon and Salva, Katrin and Schlott, Swantje and Sody, Elsa and Steingrube, Nadine and Stoffels, Ingo and Ugurel, Selma and Zaremba, Anne and Gebhardt, Christoffer and Booken, Nina and Christolouka, Maria and Buder-Bakhaya, Kristina and Bokor-Billmann, Therezia and Alexander Enk and Gholam, Patrick and H{\"a}n{\ss}le, Holger and Salzmann, Martin and Sch{\"a}fer, Sarah and Sch{\"a}kel, Knut and Schank, Timo and Bohne, Ann-Sophie and Deffaa, Sophia and Drerup, Katharina and Egberts, Friederike and Erkens, Anna-Sophie and Ewald, Benjamin and Falkvoll, Sandra and Gerdes, Sascha and Harde, Viola and Hauschild, Axel and Jost, Marion and Kosova, Katja and Messinger, Laetitia and Metzner, Malte and Morrison, Kirsten and Motamedi, Rogina and Pinczker, Anja and Rosenthal, Anne and Scheller, Natalie and Schwarz, Thomas and St{\"o}lzl, Dora and Thielking, Federieke and Tomaschewski, Elena and Wehkamp, Ulrike and Weichenthal, Michael and Wiedow, Oliver and B{\"a}r, Claudia Maria and Bender-S{\"a}belkampf, Sophia and Horbr{\"u}gger, Marc and Karoglan, Ante and Kraas, Luise and Faulhaber, J{\"o}rg and Geraud, Cyrill and Guo, Ze and Koch, Philipp and Linke, Miriam and Maurier, Nolwenn and M{\"u}ller, Verena and Thomas, Benjamin and Jochen Sven Utikal and Alamri, Ali Saeed M. and Baczako, Andrea and Berking, Carola and Betke, Matthias and Haas, Carolin and Hartmann, Daniela and Heppt, Markus V. and Kilian, Katharina and Krammer, Sebastian and Lapczynski, Natalie Lidia and Mastnik, Sebastian and Nasifoglu, Suzan and Ruini, Cristel and Sattler, Elke and Schlaak, Max and Wolff, Hans and Achatz, Birgit and Bergbreiter, Astrid and Drexler, Konstantin and Ettinger, Monika and Haferkamp, Sebastian and Halupczok, Anna and Hegemann, Marie and Dinauer, Verena and Maagk, Maria and Mickler, Marion and Philipp, Biance and Wilm, Anna and Wittmann, Constanze and Gesierich, Anja and Glutsch, Valerie and Kahlert, Katrin and Kerstan, Andreas and Schilling, Bastian and Schr{\"u}fer, Philipp} } @article {2019, title = {The future digital work force: Robotic process automation (RPA)}, journal = {Journal of Information Systems and Technology Management}, volume = {16}, year = {2019}, month = {Apr-01-2019}, doi = {10.4301/S1807-1775201916001}, author = {Madakam, Somayya and M. Holmukhe, Rajesh and Kumar Jaiswal, Durgesh} } @proceedings {2019, title = {How to Streamline AI Application in Government? A Case Study on Citizen Participation in Germany}, volume = {Lecture Notes in Computer Science 11685}, year = {2019}, pages = {233-247}, publisher = {Springer International Publishing}, address = {Cham}, abstract = {Artificial intelligence (AI) technologies are on the rise in almost every aspect of society, business and government. Especially in government, it is of interest how the application of AI can be streamlined: at least, in a controlled environment, in order to be able to evaluate potential (positive and negative) impact. Unfortunately, reuse in development of AI applications and their evaluation results lack interoperability and transferability. One potential remedy to this challenge would be to apply standardized artefacts: not only on a technical level, but also on an organization or semantic level. This paper presents findings from a qualitative explorative case study on online citizen participation in Germany that reveal insights on the current standardization level of AI applications. In order to provide an in-depth analysis, the research involves evaluation of two particular AI approaches to natural language processing. Our findings suggest that standardization artefacts for streamlining AI application exist predominantly on a technical level and are still limited.}, isbn = {978-3-030-27324-8}, issn = {0302-9743}, doi = {10.1007/978-3-030-27325-5}, author = {Balta, Dian and Kuhn, Peter and Sellami, Mahdi and Kulus, Daniel and Lieven, Claudius and Krcmar, Helmut}, editor = {Lindgren, Ida and Janssen, Marijn and Lee, Habin and Polini, Andrea and Rodr{\'\i}guez Bol{\'\i}var, Manuel Pedro and Scholl, Hans Jochen and Tambouris, Efthimios} } @article {2019, title = {In the age of the smart artificial intelligence: AI{\textquoteright}s dual capacities for automating and informating work}, journal = {Business Information Review}, year = {2019}, month = {Dec-10-2020}, pages = {026638211988399}, publisher = {Sage}, abstract = {Recent developments in artificial intelligence (AI) have generated tidal waves, which are shaking the foundation of organizations and businesses. Even though AI is considered an unprecedented disruptive force for work automation, much can be learned from current research on the computerization of work. Drawing on the seminal work of Shoshana Zuboff, this article provides a balanced perspective on the dual affordances of AI systems for automating and informating work. Whereas AI offers unique capacities for automating cognitive work that once required high-skill workers, it may be a source of unintended consequences such as cognitive complacency or de-skilling workers. To overcome such effects, the informating capacities of AI systems can be invoked to augment work, generate a more comprehensive perspective on organization, and finally equip workers with new sets of intellectual skills. }, keywords = {Algorithmic management, artificial intelligence, augmentation, automating, informating, intelligent machine}, issn = {0266-3821}, doi = {10.1177/0266382119883999}, url = {https://journals.sagepub.com/doi/abs/10.1177/0266382119883999}, attachments = {https://waim.network/sites/crowston.syr.edu/files/In\%20the\%20Age\%20of\%20the\%20Smart\%20Artificial\%20Intelligence\%20AI\%E2\%80\%99s\%20Dual\%20Capacities\%20for\%20Automating\%20and\%20Informating.pdf}, author = {Jarrahi, Mohammad Hossein} } @article {2019, title = {A Machine Learning Platform to Optimize the Translation of Personalized Network Models to the Clinic}, journal = {JCO Clinical Cancer Informatics}, year = {2019}, month = {Jan-04-2019}, pages = {1 - 17}, issn = {2473-4276}, doi = {10.1200/CCI.18.00056}, author = {Salvucci, Manuela and Rahman, Arman and Resler, Alexa J. and Udupi, Girish M. and McNamara, Deborah A. and Kay, Elaine W. and Laurent-Puig, Pierre and Longley, Daniel B. and Johnston, Patrick G. and Lawler, Mark and Wilson, Richard and Salto-Tellez, Manuel and Van Schaeybroeck, Sandra and Rafferty, Mairin and Gallagher, William M. and Rehm, Markus and Prehn, Jochen H.M.} } @article {2019, title = {Rise of the Machines: Advances in Deep Learning for Cancer Diagnosis}, journal = {Trends in Cancer}, volume = {5}, year = {2019}, month = {Jan-03-2019}, pages = {157 - 169}, issn = {24058033}, doi = {10.1016/j.trecan.2019.02.002}, author = {Levine, Adrian B. and Schlosser, Colin and Grewal, Jasleen and Coope, Robin and Jones, Steve J.M. and Yip, Stephen} } @book {2019, title = {Steps toward a scaffolding design framework}, year = {2019}, pages = {149 - 161}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, address = {Cham}, isbn = {978-3-030-12333-8}, issn = {1571-5035}, doi = {10.1007/978-3-030-12334-5_5}, author = {Correia, Ant{\'o}nio and Jameel, Shoaib and Paredes, Hugo and Fonseca, Benjamim and Schneider, Daniel}, editor = {Khan, Vassillis-Javed and Papangelis, Konstantinos and Lykourentzou, Ioanna and Markopoulos, Panos} } @article {2019, title = {Use of Artificial Intelligence to Improve Resilience and Preparedness Against Adverse Flood Events}, journal = {Water}, volume = {11}, year = {2019}, month = {Jan-05-2019}, pages = {973}, doi = {10.3390/w11050973}, author = {Saravi, Sara and Kalawsky, Roy and Joannou, Demetrios and Rivas Casado, Monica and Fu, Guangtao and Meng, Fanlin} } @article {2019, title = {Why we are failing to understand the societal impact of artificial intelligence}, journal = {Social research: An international quarterly}, volume = {86}, year = {2019}, pages = {pp. 477-498}, url = {https://muse.jhu.edu/article/732186}, author = {Lorena Jaume-Palasi} } @article {2018, title = {Advances in the development of a cognitive user interface}, journal = {MATEC Web of Conferences}, volume = {161}, year = {2018}, month = {Jan-01-2018}, pages = {01003}, doi = {10.1051/matecconf/201816101003}, url = {https://www.matec-conferences.org/10.1051/matecconf/201816101003https://www.matec-conferences.org/10.1051/matecconf/201816101003/pdf}, author = {Jokisch, Oliver and Huber, Markus}, editor = {Ronzhin, A. and Shishlakov, V.} } @conference {2018, title = {AI Assistants as Non-human Actors in Service Design}, booktitle = {21st DMI: Academic Design Management Conference}, year = {2018}, url = {https://www.researchgate.net/publication/326997609_AI_Assistants_as_Non-human_Actors_in_Service_Design?enrichId=rgreq-80a84e881501993f8b87a3d6387900aa-XXX\&enrichSource=Y292ZXJQYWdlOzMyNjk5NzYwOTtBUzo2NTkyMjU4MjI5MTY2MTBAMTUzNDE4MzA5Mjk2MQ\%3D\%3D\&el=1_x_2\&_}, author = {TiTitta Jylkastta Jylk{\"a}s and Mikko {\"A}ij{\"a}l{\"a} and Tytti Vuorikari and V{\'e}saal Rajab} } @article {2018, title = {Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making}, journal = {Business Horizons}, volume = {61}, year = {2018}, publisher = {Elsevier}, edition = {586}, chapter = {577}, abstract = {Artificial intelligence (AI) has penetrated many organizational processes, resulting in a growing fear that smart machines will soon replace many humans in decision making. To provide a more proactive and pragmatic perspective, this article highlights the complementarity of humans and AI and examines how each can bring their own strength in organizational decision-making process typically characterized by uncertainty, complexity, and equivocality. WIth a greater computational information processing capacity and an analytical approach, AI can extend humans{\textquoteright} cognition when addressing complexity, whereas human can still offer a more holistic, intuitive approach in dealing with uncertainty and equivocality in organizational decision making. This premise mirrors the idea of intelligence augmentation, which states that AI systems should be designed with the intention of augmenting, not replacing, human contribution.}, keywords = {Analytical and intuitive decision making, artificial intelligence, Human augmentation, Human-machine symbiosis, Organization decision making}, doi = {10.1016/j.bushor.2018.03.007}, author = {Mohammad Hossein Jarrahi} } @article {2018, title = {Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making}, journal = {Business Horizons}, volume = {61}, year = {2018}, month = {Jan-07-2018}, pages = {577 - 586}, issn = {00076813}, doi = {10.1016/j.bushor.2018.03.007}, author = {Jarrahi, Mohammad Hossein} } @article {2018, title = {Artificial Intelligence in Cardiology}, journal = {Journal of the American College of Cardiology}, volume = {71}, year = {2018}, month = {Jan-06-2018}, pages = {2668 - 2679}, issn = {07351097}, doi = {10.1016/j.jacc.2018.03.521}, author = {Johnson, Kipp W. and Torres Soto, Jessica and Glicksberg, Benjamin S. and Shameer, Khader and Miotto, Riccardo and Ali, Mohsin and Ashley, Euan and Joel T. Dudley} } @article {2018, title = {Artificial intelligence, machine learning and the evolution of healthcare A bright future or cause for concern?}, journal = {British Journal of Radiology}, volume = {7}, year = {2018}, pages = {223-225}, type = {Editorial}, keywords = {artificial intelligence, deep learning, machine learning}, author = {L.D. Jones and D. Golan and S. A. Hanna and M. Ramachandran} } @article {2018, title = {Artificial intelligence, machine learning and the evolution of healthcare}, journal = {Bone \& Joint Research}, volume = {7}, year = {2018}, month = {Jan-03-2018}, pages = {223 - 225}, doi = {10.1302/2046-3758.73.BJR-2017-0147.R1}, author = {Jones, L. D. and D. Golan and S. A. Hanna and M. Ramachandran} } @article {2018, title = {As s essing the impact of machine intelligence on human behaviour: an interdisciplinary endeavour}, year = {2018}, url = {https://arxiv.org/ftp/arxiv/papers/1806/1806.03192.pdf}, author = {Emilia G{\'o}mez and Carlos Castillo and Vicky Charisi and Ver{\'o}nica Dahl and Gustavo Deco and Blagoj Delipetrev and Nicole Dewandre and Miguel {\'A}ngel Gonz{\'a}lez-Ballester and Fabien Gouyon and Jos{\'e} Hern{\'a}ndez-Orallo and Perfecto Herrera and Anders Jonsson and Ansgar Koene and Martha Larson and Ram{\'o}n L{\'o}pez de M{\'a}ntaras and Bertin Martens and Marius Miron and Rub{\'e}n Moreno-Bote and Nuria Oliver and Antonio Puertas Gallardo and Heike Schweitzer and Nuria Sebastian and Xavier Serra and Joan Serr{\`a} and Song{\"u}l Tolan and Karina Vold} } @article {2018, title = {How to respond to the fourth industrial revolution, or the second information technology revolution? Dynamic new combinations between technology, market, and society through open innovation}, journal = {Journal of Open Innovation: Technology, Market, and Complexity}, volume = {4}, year = {2018}, month = {Jan-09-2018}, pages = {21}, doi = {10.3390/joitmc4030021}, author = {Lee, MinHwa and Yun, JinHyo and Pyka, Andreas and Won, DongKyu and Kodama, Fumio and Schiuma, Giovanni and Park, HangSik and Jeon, Jeonghwan and Park, KyungBae and Jung, KwangHo and Yan, Min-Ren and Lee, SamYoul and Zhao, Xiaofei} } @article {2018, title = {Improving palliative care with deep learning}, journal = {BMC Medical Informatics and Decision Making}, volume = {18}, year = {2018}, month = {Jan-12-2018}, doi = {10.1186/s12911-018-0677-8}, author = {Avati, Anand and Jung, Kenneth and Harman, Stephanie and Downing, Lance and Ng, Andrew and Shah, Nigam H.} } @article {2017, title = {Artificial intelligence in healthcare: past, present and future}, journal = {Stroke and Vascular Neurology}, volume = {2}, year = {2017}, month = {Aug-12-2018}, pages = {230 - 243}, issn = {2059-8688}, doi = {10.1136/svn-2017-000101}, author = {Jiang, Fei and Jiang, Yong and Zhi, Hui and Dong, Yi and Li, Hao and Ma, Sufeng and Wang, Yilong and Dong, Qiang and Shen, Haipeng and Wang, Yongjun} } @article {2017, title = {Artificial intelligence in research}, journal = {Science}, volume = {357}, year = {2017}, month = {Jul-07-2017}, pages = {28 - 30}, issn = {0036-8075}, doi = {10.1126/science.357.6346.28}, author = {Musib, Mrinal and Wang, Feng and Tarselli, Michael A. and Yoho, Rachel and Yu, Kun-Hsing and Andr{\'e}s, Rigoberto Medina and Greenwald, Noah F. and Pan, Xubin and Lee, Chien-Hsiu and Zhang, Jian and Dutton-Regester, Ken and Johnston, Jake Wyatt and Sharafeldin, Icell Mahmoud} } @article {2017, title = {Deep learning for healthcare: review, opportunities and challenges}, journal = {Briefings in Bioinformatics}, volume = {19}, year = {2017}, month = {Jun-05-2017}, pages = {1236 - 1246}, issn = {1467-5463}, doi = {10.1093/bib/bbx044}, author = {Miotto, Riccardo and Wang, Fei and Wang, Shuang and Jiang, Xiaoqian and Joel T. Dudley} } @article {2017, title = {One hundred years of work design research: Looking back and looking forward}, journal = {Journal of Applied Psychology}, volume = {102}, year = {2017}, month = {Jan-01-2017}, pages = {403 - 420}, issn = {0021-9010}, doi = {10.1037/apl0000106}, author = {Parker, Sharon K. and Morgeson, Frederick P. and Johns, Gary} } @article {2016, title = {Machine learning and decision support in critical care}, journal = {Proceedings of the IEEE}, volume = {104}, year = {2016}, month = {Jan-02-2016}, pages = {444 - 466}, issn = {0018-9219}, doi = {10.1109/JPROC.2015.2501978}, author = {Johnson, Alistair E. W. and Ghassemi, Mohammad M. and Nemati, Shamim and Niehaus, Katherine E. and Clifton, David and Clifford, Gari D.} } @article {2016, title = {Preparing for the future of Artificial intelligence}, year = {2016}, month = {10/2016}, institution = {Executive Office of the President National Science and Technology Council Committee on Technology}, url = {https://obamawhitehouse.archives.gov/sites/default/files/whitehouse_files/microsites/ostp/NSTC/preparing_for_the_future_of_ai.pdf} } @conference {2015, title = {Intelligible Models for HealthCare}, booktitle = {the 21th ACM SIGKDD International ConferenceProceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD {\textquoteright}15}, year = {2015}, pages = {1721 - 1730}, publisher = {ACM Press}, organization = {ACM Press}, address = {Sydney, NSW, AustraliaNew York, New York, USA}, isbn = {9781450336642}, doi = {10.1145/278325810.1145/2783258.2788613}, author = {Caruana, Rich and Lou, Yin and Gehrke, Johannes and Koch, Paul and Sturm, Marc and Elhadad, Noemie} } @article {2014, title = {Human-agent collectives}, journal = {Communications of the ACM}, volume = {57}, year = {2014}, month = {Feb-11-2016}, pages = {80 - 88}, issn = {00010782}, doi = {10.1145/269296510.1145/2629559}, author = {Jennings, N. R. and Moreau, L. and Nicholson, D. and Ramchurn, S. and Roberts, S. and Rodden, T. and Rogers, A.} } @book {2008, title = {Handbook of transdisciplinary research}, year = {2008}, pages = {427 - 432}, publisher = {Springer Netherlands}, organization = {Springer Netherlands}, address = {Dordrecht}, isbn = {978-1-4020-6698-6}, doi = {10.1007/978-1-4020-6699-3_28}, editor = {Hadorn, Gertrude Hirsch and Hoffmann-Riem, Holger and Biber-Klemm, Susette and Grossenbacher-Mansuy, Walter and Joye, Dominique and Pohl, Christian} } @inbook {1981, title = {The socio-technical perspective: The evolution of socio-technical systems as a conceptual framework and action research program}, booktitle = {Perspectives in Organization Design and Behavior}, year = {1981}, pages = {32{\textendash}47}, publisher = {John Wiley}, organization = {John Wiley}, address = {London}, author = {Trist, E.}, editor = {van de Ven, A.H. and Joyce, W. F.} }