@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 {Roccetti2020, title = {A Cautionary Tale for Machine Learning Design: why we Still Need Human-Assisted Big Data Analysis}, journal = {Mobile Networks and Applications}, volume = {25}, number = {3}, year = {2020}, pages = {1075{\textendash}1083}, publisher = {Mobile Networks and Applications}, abstract = {Supervised Machine Learning (ML) requires that smart algorithms scrutinize a very large number of labeled samples before they can make right predictions. And this is not always true either. In our experience, in fact, a neural network trained with a huge database comprised of over fifteen million water meter readings had essentially failed to predict when a meter would malfunction/need disassembly based on a history of water consumption measurements. With a second step, we developed a methodology, based on the enforcement of a specialized data semantics, that allowed us to extract only those samples for training that were not noised by data impurities. With this methodology, we re-trained the neural network up to a prediction accuracy of over 80\%. Yet, we simultaneously realized that the new training dataset was significantly different from the initial one in statistical terms, and much smaller, as well. We had reached a sort of paradox: We had alleviated the initial problem with a better interpretable model, but we had changed the replicated form of the initial data. To reconcile that paradox, we further enhanced our data semantics with the contribution of field experts. This has finally led to the extrapolation of a training dataset truly representative of regular/defective water meters and able to describe the underlying statistical phenomenon, while still providing an excellent prediction accuracy of the resulting classifier. At the end of this path, the lesson we have learnt is that a human-in-the-loop approach may significantly help to clean and re-organize noised datasets for an empowered ML design experience.}, keywords = {Human-in-the-loop methods, Human-machine-Bigdata interaction loop, Machine learning design, Smart data, Water metering and consumption}, issn = {15728153}, doi = {10.1007/s11036-020-01530-6}, author = {Roccetti, Marco and Delnevo, Giovanni and Casini, Luca and Salomoni, Paola} } @article {Acemoglu2020, title = {Competing with Robots: Firm-Level Evidence from France}, journal = {AEA Papers and Proceedings}, volume = {110}, year = {2020}, pages = {383{\textendash}388}, abstract = {We study the firm-level implications of robot adoption in France. Of 55,390 firms in our sample, 598 adopted robots between 2010 and 2015, but these firms accounted for 20 percent of manufacturing employment. Adopters experienced significant declines in labor shares, the share of production workers in employment, and increases in value added and productivity. They expand their overall employment as well. However, this expansion comes at the expense of competitors, leading to an overall negative association between adoption and employment. Robot adoption has a large impact on the labor share because adopters are larger and grow faster than their competitors.}, keywords = {automation, competition, j23, j24, jel codes, l11, labor share, manufacturing, productivity, reallocation, robots, tasks}, issn = {2574-0768}, doi = {10.1257/pandp.20201003}, author = {Acemoglu, Daron and Lelarge, Claire and Restrepo, Pascual} } @article {Bansal2020, title = {Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance}, year = {2020}, abstract = {

Increasingly, organizations are pairing humans with AI systems to improve decision-making and reducing costs. Proponents of human-centered AI argue that team performance can even further improve when the AI model explains its recommendations. However, a careful analysis of existing literature reveals that prior studies observed improvements due to explanations only when the AI, alone, outperformed both the human and the best human-AI team. This raises an important question: can explanations lead to complementary performance, i.e., with accuracy higher than both the human and the AI working alone? We address this question by devising comprehensive studies on human-AI teaming, where participants solve a task with help from an AI system without explanations and from one with varying types of AI explanation support. We carefully controlled to ensure comparable human and AI accuracy across experiments on three NLP datasets (two for sentiment analysis and one for question answering). While we found complementary improvements from AI augmentation, they were not increased by state-of-the-art explanations compared to simpler strategies, such as displaying the AI{\textquoteright}s confidence. We show that explanations increase the chance that humans will accept the AI{\textquoteright}s recommendation regardless of whether the AI is correct. While this clarifies the gains in team performance from explanations in prior work, it poses new challenges for human-centered AI: how can we best design systems to produce complementary performance? Can we develop explanatory approaches that help humans decide whether and when to trust AI input?

}, url = {http://arxiv.org/abs/2006.14779}, author = {Bansal, Gagan and Wu, Tongshuang and Zhou, Joyce and Fok, Raymond and Nushi, Besmira and Kamar, Ece and Ribeiro, Marco Tulio and Weld, Daniel S.} } @article {Huang2020, title = {Engaged to a Robot? The Role of AI in Service}, journal = {Journal of Service Research}, year = {2020}, abstract = {This article develops a strategic framework for using artificial intelligence (AI) to engage customers for different service benefits. This framework lays out guidelines of how to use different AIs to engage customers based on considerations of nature of service task, service offering, service strategy, and service process. AI develops from mechanical, to thinking, and to feeling. As AI advances to a higher intelligence level, more human service employees and human intelligence (HI) at the intelligence levels lower than that level should be used less. Thus, at the current level of AI development, mechanical service should be performed mostly by mechanical AI, thinking service by both thinking AI and HI, and feeling service mostly by HI. Mechanical AI should be used for standardization when service is routine and transactional, for cost leadership, and mostly at the service delivery stage. Thinking AI should be used for personalization when service is data-rich and utilitarian, for quality leadership, and mostly at the service creation stage. Feeling AI should be used for relationalization when service is relational and high touch, for relationship leadership, and mostly at the service interaction stage. We illustrate various AI applications for the three major AI benefits, providing managerial guidelines for service providers to leverage the advantages of AI as well as future research implications for service researchers to investigate AI in service from modeling, consumer, and policy perspectives.}, keywords = {artificial intelligence, augmentation, automation, engagement, feeling AI, human intelligence, mechanical AI, personalization, relationalization, replacement, robots, service process, service strategy, standardization, thinking AI}, issn = {15527379}, doi = {10.1177/1094670520902266}, author = {Huang, Ming Hui and Rust, Roland T} } @article {Reid-musson2020, title = {Feminist economic geography and the future of work}, journal = {EPA: Economy and Space}, year = {2020}, pages = {1{\textendash}12}, keywords = {corresponding author, department of geography, emily reid-musson, feminist economic geography, memorial university of newfoundland, newfoundland and labrador a1c, social reproduction, subjectivity, technology, work}, doi = {10.1177/0308518X20947101}, author = {Reid-musson, Emily and Cockayne, Daniel and Frederiksen, Lia and Worth, Nancy} } @article {Reim2020, title = {Implementation of Artificial Intelligence (AI): A Roadmap for Business Model Innovation}, journal = {AI}, volume = {1}, number = {2}, year = {2020}, pages = {180{\textendash}191}, abstract = {Technical advancements within the subject of artificial intelligence (AI) leads towards development of human-like machines, able to operate autonomously and mimic our cognitive behavior. The progress and interest among managers, academics and the public has created a hype among many industries, and many firms are investing heavily to capitalize on the technology through business model innovation. However, managers are left with little support from academia when aiming to implement AI in their firm{\textquoteright}s operations, which leads to an increased risk of project failure and unwanted results. This paper aims to provide a deeper understanding of AI and how it can be used as a catalyst for business model innovation. Due to the increasing range and variety of the available published material, a literature review has been performed to gather current knowledge within AI business model innovation. The results are presented in a roadmap to guide the implementation of AI to firm{\textquoteright}s operations. Our presented findings suggest four steps when implementing AI: (1) understand AI and organizational capabilities needed for digital transformation; (2) understand current BM, potential for BMI, and business ecosystem role; (3) develop and refine capabilities needed to implement AI; and (4) reach organizational acceptance and develop internal competencies.}, keywords = {artificial intelligence, business model innovation, business models, implementation}, issn = {2673-2688}, doi = {10.3390/ai1020011}, author = {Reim, Wiebke and {\r A}str{\"o}m, Josef and Eriksson, Oliver} } @conference {Das2020, title = {Learning occupational task-shares dynamics for the future of work}, booktitle = {AIES 2020 - Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society}, year = {2020}, pages = {36{\textendash}42}, abstract = {The recent wave of AI and automation has been argued to differ from previous General Purpose Technologies (GPTs), in that it may lead to rapid change in occupations{\textquoteright} underlying task requirements and persistent technological unemployment. In this paper, we apply a novel methodology of dynamic task shares to a large dataset of online job postings to explore how exactly occupational task demands have changed over the past decade of AI innovation, especially across high, mid and low wage occupations. Notably, big data and AI have risen significantly among high wage occupations since 2012 and 2016, respectively. We built an ARIMA model to predict future occupational task demands and showcase several relevant examples in Healthcare, Administration, and IT. Such task demands predictions across occupations will play a pivotal role in retraining the workforce of the future.}, keywords = {AI, automation, future of work, Occupational Task Demands}, isbn = {9781450371100}, doi = {10.1145/3375627.3375826}, author = {Das, Subhro and Steffen, Sebastian and Clarke, Wyatt and Reddy, Prabhat and Brynjolfsson, Erik and Fleming, Martin} } @article {Seeber2020, title = {Machines as teammates: A research agenda on AI in team collaboration}, journal = {Information and Management}, volume = {57}, number = {2}, year = {2020}, pages = {103174}, publisher = {Elsevier}, abstract = {What if artificial intelligence (AI) machines became teammates rather than tools? This paper reports on an international initiative by 65 collaboration scientists to develop a research agenda for exploring the potential risks and benefits of machines as teammates (MaT). They generated 819 research questions. A subteam of 12 converged them to a research agenda comprising three design areas {\textendash} Machine artifact, Collaboration, and Institution {\textendash} and 17 dualities {\textendash} significant effects with the potential for benefit or harm. The MaT research agenda offers a structure and archetypal research questions to organize early thought and research in this new area of study.}, keywords = {artificial intelligence, Design, Duality, Research agenda, Team collaboration}, issn = {03787206}, doi = {10.1016/j.im.2019.103174}, url = {https://doi.org/10.1016/j.im.2019.103174}, author = {Seeber, Isabella and Bittner, Eva and Briggs, Robert O and de Vreede, Triparna and de Vreede, Gert Jan and Elkins, Aaron and Maier, Ronald and Merz, Alexander B and Oeste-Rei{\ss}, Sarah and Randrup, Nils and Schwabe, Gerhard and S{\"o}llner, Matthias} } @booklet {Riemer2020, title = {The robo-apocalypse plays out in the quality, not in the quantity of work}, howpublished = {Journal of Information Technology}, year = {2020}, issn = {14664437}, doi = {10.1177/0268396220923677}, author = {Riemer, Kai and Peter, Sandra} } @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 = {Artificial intelligence and the future of labor demand}, year = {2019}, institution = {National Bureau of Economic Research}, address = {Cambridge, MA}, doi = {10.3386/w25682}, author = {Acemoglu, Daron and Restrepo, Pascual} } @article {2019, title = {Automation and new tasks: How technology displaces and reinstates labor}, year = {2019}, institution = {National Bureau of Economic Research}, address = {Cambridge, MA}, doi = {10.3386/w25684}, author = {Acemoglu, Daron and Restrepo, Pascual} } @conference {2019, title = {Consider the human work experience when integrating robotics in the workplace}, booktitle = {2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI)2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI)}, year = {2019}, pages = {75 - 84}, publisher = {IEEE}, organization = {IEEE}, address = {Daegu, Korea (South)}, doi = {10.1109/HRI.2019.8673139}, url = {https://ieeexplore.ieee.org/document/8673139/http://xplorestaging.ieee.org/ielx7/8666012/8673065/08673139.pdf?arnumber=8673139}, author = {Welfare, Katherine S. and Hallowell, Matthew R. and Shah, Julie A. and Riek, Laurel D.} } @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 of the work in America}, year = {2019}, month = {07/2019}, keywords = {consulting reports}, url = {https://www.mckinsey.com/featured-insights/future-of-work/the-future-of-work-in-america-people-and-places-today-and-tomorrow}, author = {Susan Lund and James Manyika and Liz Hilton Segal and Andre Dua and Bryan Hancock and Scott Rutherford and Brent Macon} } @article {2019, title = {Global commission on the future of work}, year = {2019}, institution = {International Labour Organization }, keywords = {government reports}, url = {https://www.ilo.org/global/topics/future-of-work/WCMS_569528/lang--en/index.htm}, author = {Cyril Ramaphosa and Stefan Lofven} } @conference {2019, title = {IBM Talent Management Solutions The role of AI in mitigating bias to enhance diversity and inclusion}, year = {2019}, note = {In this paper, we examine the nature of diversity and inclusion (D\&I), explore biases as an inhibitor to more diverse and inclusive workplaces, and provide insights into the role that AI can play in mitigating biases. We then offer practical recommendations to organizations who are looking to adopt AI in their HR practices.}, month = {03/2019}, url = {https://www.ibm.com/downloads/cas/2DZELQ4O}, author = {Haiyan Zhang and Sheri Feinzi and Louise Raisbec and Iain McCombe and Nigel Guenol and Jenny Montalt and Kimberley Messe} } @conference {2019, title = {Impacts of Artificial Intelligence on Public Administration: A Systematic Literature Review}, booktitle = {2019 14th Iberian Conference on Information Systems and Technologies (CISTI)}, year = {2019}, isbn = {978-989-98434-9-3}, url = {https://ieeexplore.ieee.org/document/8760778}, author = {Jo{\~a}o Reis and Paula Esp{\'\i}rito Santo and Lisbon, Portugal and Nuno Mel{\~a}o} } @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 = {Mitigating bias in algorithmic employment screening: Evaluating claims and practices}, year = {2019}, abstract = {

There has been rapidly growing interest in the use of algorithms for employment assessment,especially as a means to address or mitigate bias in hiring. Yet, to date, little is known abouthow these methods are being used in practice. How are algorithmic assessments built, vali-dated, and examined for bias? In this work, we document and assess the claims and practicesof companies offering algorithms for employment assessment, using a methodology that can beapplied to evaluate similar applications and issues of bias in other domains. In particular, weidentify vendors of algorithmic pre-employment assessments (i.e., algorithms to screen candi-dates), document what they have disclosed about their development and validation procedures,and evaluate their techniques for detecting and mitigating bias. We find that companies{\textquoteright} for-mulation of {\textquotedblleft}bias{\textquotedblright} varies, as do their approaches to dealing with it. We also discuss the variouschoices vendors make regarding data collection and prediction targets, in light of the risks andtrade-offs that these choices pose. We consider the implications of these choices and we raise anumber of technical and legal considerations.

}, keywords = {social power of algorithms}, url = {https://www.researchgate.net/publication/333971698_Mitigating_Bias_in_Algorithmic_Employment_Screening_Evaluating_Claims_and_Practices}, author = {Manish Raghavan and Solon Barocas and Jon KleinbergKaren Levy} } @article {2019, title = {Physician perspectives on integration of artificial intelligence into diagnostic pathology}, journal = {npj Digital Medicine}, volume = {2}, year = {2019}, month = {Jan-12-2019}, doi = {10.1038/s41746-019-0106-0}, author = {Sarwar, Shihab and Dent, Anglin and Faust, Kevin and Richer, Maxime and Djuric, Ugljesa and Van Ommeren, Randy and Diamandis, Phedias} } @article {2019, title = {Pooling knowledge through artificial swarm intelligence to improve business decision making}, journal = {California Management Review}, volume = {61}, year = {2019}, month = {Sep-08-2019}, pages = {84 - 109}, issn = {0008-1256}, doi = {10.1177/0008125619862256}, author = {Metcalf, Lynn and Askay, David A. and Rosenberg, Louis B.} } @article {2019, title = {Primer on artificial intelligence and robotics}, journal = {Journal of Organization Design}, volume = {8}, year = {2019}, month = {Jan-12-2019}, doi = {10.1186/s41469-019-0050-0}, author = {Raj, Manav and Seamans, Robert} } @article {2019, title = {Questions for artificial intelligence in health care}, journal = {JAMA}, volume = {321}, year = {2019}, month = {Jan-01-2019}, pages = {31}, issn = {0098-7484}, doi = {10.1001/jama.2018.18932}, author = {Maddox, Thomas M. and Rumsfeld, John S. and Payne, Philip R. O.} } @article {2019, title = {A Review of the Social Impacts and Ethical Implications of Artificial Intelligence}, year = {2019}, publisher = {Global AI Ethics}, keywords = {ethics}, url = {https://arxiv.org/ftp/arxiv/papers/1907/1907.07892.pdf}, author = {Alexa Hagerty and Igor Rubinov} } @book {2019, title = {Toward a conceptual framework for understanding AI action and legal reaction}, volume = {11489}, year = {2019}, pages = {453 - 459}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, address = {Cham}, isbn = {978-3-030-18304-2}, issn = {0302-9743}, doi = {10.1007/978-3-030-18305-9_44}, author = {Dahya, Raheena and Morris, Alexis}, editor = {Meurs, Marie-Jean and Rudzicz, Frank} } @article {Frank2019, title = {Toward understanding the impact of artificial intelligence on labor}, journal = {Proceedings of the National Academy of Sciences}, volume = {116}, year = {2019}, month = {apr}, pages = {6531{\textendash}6539}, abstract = {

Rapid advances in artificial intelligence (AI) and automation technologies have the potential to significantly disrupt labor markets. While AI and automation can augment the productivity of some workers, they can replace the work done by others and will likely transform almost all occupations at least to some degree. Rising automation is happening in a period of growing economic inequality, raising fears of mass technological unemployment and a renewed call for policy efforts to address the consequences of technological change. In this paper we discuss the barriers that inhibit scientists from measuring the effects of AI and automation on the future of work. These barriers include the lack of high-quality data about the nature of work (e.g., the dynamic requirements of occupations), lack of empirically informed models of key microlevel processes (e.g., skill substitution and human{\textendash}machine complementarity), and insufficient understanding of how cognitive technologies interact with broader economic dynamics and institutional mechanisms (e.g., urban migration and international trade policy). Overcoming these barriers requires improvements in the longitudinal and spatial resolution of data, as well as refinements to data on workplace skills. These improvements will enable multidisciplinary research to quantitatively monitor and predict the complex evolution of work in tandem with technological progress. Finally, given the fundamental uncertainty in predicting technological change, we recommend developing a decision framework that focuses on resilience to unexpected scenarios in addition to general equilibrium behavior.

}, issn = {0027-8424}, doi = {10.1073/pnas.1900949116}, url = {http://www.pnas.org/lookup/doi/10.1073/pnas.1900949116}, author = {Frank, Morgan R and Autor, David and Bessen, James E and Brynjolfsson, Erik and Cebrian, Manuel and Deming, David J and Feldman, Maryann and Groh, Matthew and Lobo, Jos{\'e} and Moro, Esteban and Wang, Dashun and Youn, Hyejin and Rahwan, Iyad} } @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} } @conference {2018, title = {AI risk mitigation through democratic governance}, booktitle = {the 2018 AAAI/ACM ConferenceProceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society - AIES {\textquoteright}18}, year = {2018}, pages = {366 - 367}, publisher = {ACM Press}, organization = {ACM Press}, address = {New Orleans, LA, USANew York, New York, USA}, isbn = {9781450360128}, doi = {10.1145/3278721.3278801}, author = {Garvey, Colin} } @article {2018, title = {Applications of machine learning algorithms to predict therapeutic outcomes in depression: A meta-analysis and systematic review}, journal = {Journal of Affective Disorders}, volume = {241}, year = {2018}, pages = {519-532}, issn = {01650327}, doi = {10.1016/j.jad.2018.08.073}, author = {Lee, Yena and Ragguett, Renee-Marie and Mansur, Rodrigo B. and Boutilier, Justin J. and Rosenblat, Joshua D. and Trevizol, Alisson and Brietzke, Elisa and Lin, Kangguang and Pan, Zihang and Subramaniapillai, Mehala and Chan, Timothy C.Y. and Fus, Dominika and Park, Caroline and Musial, Natalie and Zuckerman, Hannah and Chen, Vincent Chin-Hung and Ho, Roger and Rong, Carola and McIntyre, Roger S.} } @conference {2018, title = {Artificial General Intelligence}, year = {2018}, url = {https://ivizlab.org/wp-content/uploads/sites/2/2018/09/TurnerDiPaola_2018_ArtificialGeneralIntelligence.pdf}, author = {Matthew Ikl{\'e} and Arthur Franz and Rafal Rzepka and Ben Goertzel} } @article {2018, title = {Artificial Intelligence in Service}, journal = {Journal of Service Research}, volume = {21}, year = {2018}, month = {May-05-2018}, pages = {155 - 172}, issn = {1094-6705}, doi = {10.1177/1094670517752459}, author = {Huang, Ming-Hui and Rust, Roland T.} } @article {2018, title = {Artificial Intelligence Is Automating Hollywood}, year = {2018}, month = {07/2018}, keywords = {AI use cases}, url = {https://futurism.com/artificial-intelligence-automating-hollywood-art}, author = {Dan Robitzki} } @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 = {Boundary spanning at the science{\textendash}policy interface: the practitioners{\textquoteright} perspectives}, journal = {Sustainability Science}, volume = {13}, year = {2018}, month = {Jan-07-2018}, pages = {1175 - 1183}, issn = {1862-4065}, doi = {10.1007/s11625-018-0550-9}, author = {Bednarek, A. T. and Wyborn, C. and Cvitanovic, C. and Meyer, R. and Colvin, R. M. and Addison, P. F. E. and Close, S. L. and Curran, K. and Farooque, M. and Goldman, E. and Hart, D. and Mannix, H. and McGreavy, B. and Parris, A. and Posner, S. and Robinson, C. and Ryan, M. and Leith, P.} } @book {2018, title = {A critical review of the politics of artificial intelligent machines, alienation and the existential risk threat to America{\textquoteright}s labour force}, volume = {10963}, year = {2018}, pages = {217 - 232}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, address = {Cham}, isbn = {978-3-319-95170-6}, issn = {0302-9743}, doi = {10.1007/978-3-319-95171-3_18}, author = {Wogu, Ikedinachi Ayodele and Misra, Sanjay and Assibong, Patrick and Adewumi, Adewole and Damasevicius, Robertas and Maskeliunas, Rytis}, editor = {Gervasi, Osvaldo and Murgante, Beniamino and Misra, Sanjay and Stankova, Elena and Torre, Carmelo M. and Rocha, Ana Maria A.C. and Taniar, David and Apduhan, Bernady O. and Tarantino, Eufemia and Ryu, Yeonseung} } @article {2018, title = {Digital economy and the models of income distribution in the society}, journal = {SHS Web of Conferences}, volume = {44}, year = {2018}, month = {Jan-01-2018}, pages = {00005}, doi = {10.1051/shsconf/20184400005}, author = {Akaev, Askar and Rudskoi, Andrew and Devezas, Tessaleno}, editor = {Sarygulov, A. and Sergeev, V. and Ungv{\'a}ri, L. and Semmler, W.} } @book {2018, title = {eHealth - Making Health Care SmarterUse of Artificial Intelligence in Healthcare Delivery}, year = {2018}, publisher = {InTech}, organization = {InTech}, isbn = {978-1-78923-522-7}, doi = {http://dx.doi.org/10.5772/intechopen.74714}, author = {Reddy, Sandeep}, editor = {Heston, Thomas F.} } @article {2018, title = {How do machine learning, robotic process automation, and blockchains affect the human factor in business process management? }, journal = {Communications of the Association for Information Systems}, year = {2018}, month = {Jan-01-2018}, pages = {297 - 320}, doi = {10.17705/1CAIS.04319}, author = {Mendling, Jan and Decker, Gero and Hull, Richard and Reijers, Hajo A. and Weber, Ingo} } @article {2018, title = {How driverless cars could disrupt the real estate industry}, year = {2018}, keywords = {autonomous vehicles}, url = {https://www.forbes.com/sites/elyrazin/2018/03/11/how-driverless-cars-could-disrupt-the-real-estate-industry/$\#$1595cb8613c1}, author = {Ely Razin} } @conference {2018, title = {The impact of artificial intelligence on the HR function}, booktitle = {IES Perspectives on HR 2018}, year = {2018}, keywords = {HR}, url = {https://www.employment-studies.co.uk/system/files/resources/files/mp142_The_impact_of_Artificial_Intelligence_on_the_HR_function-Peter_Reilly.pdf}, author = {Peter Reilly} } @article {2018, title = {Implications of technology for growth, factor shares, and employment}, journal = {American Economic Review}, volume = {108}, year = {2018}, month = {Jan-06-2018}, pages = {1488 - 1542}, issn = {0002-8282}, doi = {10.1257/aer.20160696}, author = {Acemoglu, Daron and Restrepo, Pascual} } @book {2018, title = {Investigating ergonomics in the context of human-robot collaboration as a sociotechnical system}, volume = {784}, year = {2018}, pages = {127 - 135}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, address = {Cham}, isbn = {978-3-319-94345-9}, issn = {2194-5357}, doi = {10.1007/978-3-319-94346-6_12}, author = {R{\"u}cker, Daniel and Hornfeck, R{\"u}diger and Paetzold, Kristin}, editor = {Chen, Jessie} } @article {2018, title = {Is IT changing the world? Conceptions of causality for information systems theorizing }, journal = {MIS Quarterly}, volume = {42}, year = {2018}, pages = {1255-1280}, url = {https://dl.acm.org/citation.cfm?id=3370119.3370131}, author = {M. Lynne Markus and Frantz Rowe} } @article {2018, title = {{\textquoteleft}It{\textquoteright}s either a panda or a gibbon{\textquoteright}: AI winters and the limits of deep learning}, journal = {War on the rocks}, year = {2018}, type = {Commentary }, url = {https://warontherocks.com/2018/05/its-either-a-panda-or-a-gibbon-ai-winters-and-the-limits-of-deep-learning/}, author = {Robert Richbourg} } @book {2018, title = {Lecture Notes in Computer ScienceArtificial General IntelligenceTowards a Sociological Conception of Artificial Intelligence}, volume = {10999}, year = {2018}, pages = {130 - 139}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, address = {Cham}, isbn = {978-3-319-97675-4}, issn = {0302-9743}, doi = {10.1007/978-3-319-97676-1}, author = {Jakub Mlynar and Hamed S. Alavi and Himanshu Verma and Lorenzo Cantoni}, editor = {Matthew Ikl{\'e} and Arthur Franz and Rafal Rzepka and Ben Goertzel} } @book {2018, title = {Machine Learning for Ecology and Sustainable Natural Resource ManagementUse of Machine Learning (ML) for Predicting and Analyzing Ecological and {\textquoteleft}Presence Only{\textquoteright} Data: An Overview of Applications and a Good Outlook}, year = {2018}, pages = {27 - 61}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, address = {Cham}, isbn = {978-3-319-96976-3}, doi = {10.1007/978-3-319-96978-7_2}, author = {Huettmann, Falk and Craig, Erica H. and Herrick, Keiko A. and Baltensperger, Andrew P. and Grant Humphries and Lieske, David J. and Miller, Katharine and Mullet, Timothy C. and Oppel, Steffen and Resendiz, Cynthia and Rutzen, Imme and Schmid, Moritz S. and Suwal, Madan K. and Young, Brian D.}, editor = {Grant Humphries and Magness, Dawn R. and Huettmann, Falk} } @conference {2018, title = {Machines as Teammates: A Collaboration Research Agenda}, booktitle = {Hawaii International Conference on System Sciences}, year = {2018}, publisher = {Hawaii International Conference on System Sciences}, organization = {Hawaii International Conference on System Sciences}, doi = {10.24251/HICSS.2018.055}, url = {http://hdl.handle.net/10125/49887}, attachments = {https://waim.network/sites/crowston.syr.edu/files/SeeberEtAl_2018_MachinesAsTeammates.pdf}, author = {Seeber, Isabella and Bittner, Eva and Briggs, Robert O. and de Vreede, Gert-Jan and de Vreede, Triparna and Druckenmiller, Doug and Maier, Ronald and Merz, Alexander B. and Oeste-Rei{\ss}, Sarah and Randrup, Nils and Gerhard Schwabe and S{\"o}llner, Matthias} } @article {2018, title = {Making progress with the automation of systematic reviews: principles of the International Collaboration for the Automation of Systematic Reviews (ICASR)}, journal = {Systematic Reviews}, year = {2018}, month = {2018}, publisher = {Springer}, type = {Review}, abstract = {Systematic reviews (SR) are vital to health care, but have become complicated and time-consuming, due to the rapid expansion of evidence to be synthesised. Fortunately, many tasks of systematic reviews have the potential to be automated or may be assisted by automation. Recent advances in natural language processing, text mining and machine learning have produced new algorithms that can accurately mimic human endeavour in systematic review activity, faster and more cheaply. Automation tools need to be able to work together, to exchange data and results. Therefore, we initiated the International Collaboration for the Automation of Systematic Reviews (ICASR), to successfully put all the parts of automation of systematic review production together. The first meeting was held in Vienna in October 2015. We established a set of principles to enable tools to be developed and integrated into toolkits. This paper sets out the principles devised at that meeting, which cover the need for improvement in efficiency of SR tasks, automation across the spectrum of SR tasks, continuous improvement, adherence to high quality standards, flexibility of use and combining components, the need for a collaboration and varied skills, the desire for open source, shared code and evaluation, and a requirement for replicability through rigorous and open evaluation. Automation has a great potential to improve the speed of systematic reviews. Considerable work is already being done on many of the steps involved in a review. The {\textquoteright}Vienna Principles{\textquoteright} set out in this paper aim to guide a more coordinated effort which will allow the integration of work by separate teams and build on the experience, code and evaluations done by the many teams working across the globe.}, keywords = {automation, Collaboration, Systematic review}, doi = {10.1186/s13643-018-0740-7}, author = {Elaine Beller and Justin Clark and Guy Tsafnat and Clive Adams and Heinz Diehl and Hans Lund and Mourad Ouzzani and Kristina Thayer and James Thomas and Tari Turner and Jun Xia and Karen Robinson and Paul Glasziou} } @article {2018, title = {Making progress with the automation of systematic reviews: principles of the International Collaboration for the Automation of Systematic Reviews (ICASR)}, journal = {Systematic Reviews}, volume = {7}, year = {2018}, month = {Jan-12-2018}, keywords = {science}, doi = {10.1186/s13643-018-0740-7}, author = {Elaine Beller and Justin Clark and Guy Tsafnat and Clive Adams and Heinz Diehl and Hans Lund and Mourad Ouzzani and Kristina Thayer and James Thomas and Tari Turner and Jun Xia and Karen Robinson and Paul Glasziou} } @article {2018, title = {A method to link advances in Artificial Intelligence to occupational abilities}, journal = {AEA Papers and Proceedings}, volume = {108}, year = {2018}, month = {Jan-01-2018}, pages = {54 - 57}, issn = {2574-0768}, doi = {10.1257/pandp.20181021}, author = {Felten, Edward W. and Raj, Manav and Seamans, Robert} } @article {2018, title = {The predictions on the future of labour are not grounded; some arguments for a bayesian approach}, journal = {Postmodern Openings}, volume = {9}, year = {2018}, month = {Jan-09-2020}, pages = {36 - 63}, issn = {20680236}, doi = {10.18662/po/35}, author = {Rotila, Viorel} } @article {2018, title = {The productivity j-curve: How intangibles complement general purpose technologies}, year = {2018}, institution = {National Bureau of Economic Research}, address = {Cambridge, MA}, doi = {10.3386/w25148}, author = {Brynjolfsson, Erik and Rock, Daniel and Syverson, Chad} } @conference {2018, title = {Rethinking AI strategy and policy as entangled super wicked problems}, booktitle = {the 2018 AAAI/ACM ConferenceProceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society - AIES {\textquoteright}18}, year = {2018}, pages = {122 - 122}, publisher = {ACM Press}, organization = {ACM Press}, address = {New Orleans, LA, USANew York, New York, USA}, isbn = {9781450360128}, doi = {10.1145/3278721.3278746}, author = {Gruetzemacher, Ross} } @article {2018, title = {Robot Assisted Surgical Ward Rounds: Virtually Always There}, journal = {Journal of Innovation in Health Informatics}, volume = {25}, year = {2018}, month = {Sep-03-2018}, pages = {041}, issn = {2058-4555}, doi = {10.14236/jhi.v25i1.982}, author = {Stefanie M. Croghan and Paul Carroll and Sarah Reade and Amy E Gillis and Paul F. Ridgway} } @article {2018, title = {Robotic Process Automation for Auditing}, journal = {Journal of Emerging Technologies in Accounting}, volume = {15}, year = {2018}, month = {Jan-07-2018}, pages = {1 - 10}, issn = {1554-1908}, doi = {10.2308/jeta-10589}, author = {Moffitt, Kevin C. and Rozario, Andrea M. and Vasarhelyi, Miklos A.} } @article {2017, title = {AI can take over our mundane tasks}, year = {2017}, month = {10/2017}, abstract = {

Here{\textquoteright}s how human workers can learn new, more stimulating skills

}, url = {https://www.vox.com/2017/10/18/16492156/coaching-cloud-future-work-jobs-artificial-intelligence-ai-enterprise-employee-training}, author = {Gordon Ritter} } @article {2017, title = {Artificial intelligence and the modern productivity paradox: a clash of expectations and statistics}, number = {24001}, year = {2017}, month = {11/2017}, keywords = {economics of automation}, url = {http://www.nber.org/papers/w24001}, author = {Erik Brynjolfsson and Daniel Rock and Chad Syverson} } @article {2017, title = {Artificial Intelligence: The next digital frontier}, year = {2017}, url = {https://www.mckinsey.com/~/media/McKinsey/Industries/Advanced\%20Electronics/Our\%20Insights/How\%20artificial\%20intelligence\%20can\%20deliver\%20real\%20value\%20to\%20companies/MGI-Artificial-Intelligence-Discussion-paper.ashx}, author = {Jaccques Bughin and Eric Hazan and Sree Ramaswamy and Michael Chui and Tera Allas and Peter Dahlstrom and Nicolaus Henke and Monica Trench} } @proceedings {2017, title = {Automation of a Business Process Using Robotic Process Automation (RPA): A Case Study}, volume = {742}, year = {2017}, pages = {65 - 71}, publisher = {Springer International Publishing}, address = {Cham}, isbn = {978-3-319-66962-5}, issn = {1865-0929}, doi = {10.1007/978-3-319-66963-2_7}, author = {Aguirre, Santiago and Rodriguez, Alejandro}, editor = {Figueroa-Garc{\'\i}a, Juan Carlos and L{\'o}pez-Santana, Eduyn Ramiro and Villa-Ram{\'\i}rez, Jos{\'e} Luis and Ferro-Escobar, Roberto} } @article {2017, title = {Can ford turn itself find a tech company}, year = {2017}, url = {https://www.nytimes.com/interactive/2017/11/09/magazine/tech-design-autonomous-future-cars-detroit-ford.html}, author = {Kevin Roose} } @article {2017, title = {Employment, inequality and ethics in the digital age}, journal = {The IPPR Commission on Economic Justice}, year = {2017}, month = {12/2017}, url = {https://www.ippr.org/publications/managing-automation}, author = {Mathew Lawrence and Carys Roberts and Loren King} } @article {657, title = {Gravity Spy: Integrating Advanced LIGO Detector Characterization, Machine Learning, and Citizen Science}, journal = {Classical and Quantum Gravity}, volume = {34}, year = {2017}, pages = {064003}, type = {Journal Article}, doi = {10.1088/1361-6382/aa5cea}, author = {Michael Zevin and Scott Coughlin and Sara Bahaadini and Emre Besler and Neda Rohani and Sarah Allen and Miriam Cabero and Kevin Crowston and Aggelos Katsaggelos and Shane Larson and Tae Kyoung Lee and Chris Lintott and Tyson Littenberg and Andrew Lundgren and Carsten Oesterlund and Joshua Smith and Laura Trouille and Vicky Kalogera} } @article {2017, title = {Scrutinizing an algorithmic technique: the Bayes classifier as interested reading of reality}, journal = {Information, Communication \& Society}, volume = {20}, year = {2017}, month = {Feb-01-2017}, pages = {100 - 117}, issn = {1369-118X}, doi = {10.1080/1369118X.2016.1181195}, author = {Rieder, Bernhard} } @article {2017, title = {Situated redesign in creative occupations {\textendash} An ethnography of architects}, journal = {Academy of Management Discoveries}, volume = {3}, year = {2017}, month = {Jan-12-2017}, pages = {404 - 424}, doi = {10.5465/amd.2016.0039}, author = {Rahman, Hatim A. and Barley, Stephen R.} } @conference {2016, title = {Degree of Automation in Command and Control Decision Support Systems}, booktitle = {IEEE International Conference on Systems, Man and Cybernetics}, year = {2016}, url = {https://ieeexplore.ieee.org/document/7844402}, author = {Ryan M. Robinson and Michael J. McCount and Amar R. Marathe and W. D. Nothwang and Emily A. Doucette} } @book {2016, title = {Handbook of Science and Technology ConvergenceConvergence-Divergence Process}, year = {2016}, pages = {79 - 93}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, address = {Cham}, isbn = {978-3-319-07051-3}, doi = {10.1007/978-3-319-07052-0_11}, author = {Roco, Mihail C.}, editor = {Bainbridge, William Sims and Roco, Mihail C.} } @article {2016, title = {Humans, robots and values}, journal = {Technology in Society}, volume = {45}, year = {2016}, month = {Jan-05-2016}, pages = {19 - 28}, issn = {0160791X}, doi = {10.1016/j.techsoc.2016.01.002}, author = {Cockshott, Paul and Renaud, Karen} } @article {2016, title = {Implications of technology for growth, factor shares and employment}, year = {2016}, institution = {National Bureau of Economic Research}, address = {Cambridge, MA}, doi = {10.3386/w22252}, url = {http://www.nber.org/papers/w22252.pdf}, author = {Acemoglu, Daron and Restrepo, Pascual} } @article {2016, title = {Regional Diversity in Autonomy and Work: A Case Study from Uber and Lyft Drivers}, year = {2016}, chapter = {Data And Society}, url = {https://datasociety.net/pubs/ia/Rosenblat-Hwang_Regional_Diversity-10-13.pdf}, author = {Alex Rosenblat and Tim Hwang} } @conference {2016, title = {SPIE ProceedingsAn efficient fusion approach for combining human and machine decisions}, booktitle = {SPIE Defense + SecurityMicro- and Nanotechnology Sensors, Systems, and Applications VIII}, volume = {9836}, year = {2016}, pages = {983621}, publisher = {SPIE}, organization = {SPIE}, address = {Baltimore, Maryland, United States}, doi = {10.1117/12.2220788}, url = {http://proceedings.spiedigitallibrary.org/proceeding.aspx?doi=10.1117/12.2220788}, author = {Lee, Hyungtae and Kwon, Heesung and Ryan M. Robinson and W. D. Nothwang and Amar R. Marathe}, editor = {George, Thomas and Dutta, Achyut K. and Islam, M. Saif} } @article {2015, title = {Artificial intelligence for the real world: Don{\textquoteright}t start with moon shots}, year = {2015}, keywords = {consulting reports}, url = {https://hbr.org/2018/01/artificial-intelligence-for-the-real-world}, author = {Thomas H. Davenport and Rajeev Ronanki} } @article {RN30644, title = {Human-level control through deep reinforcement learning}, journal = {Nature}, volume = {518}, number = {7540}, year = {2015}, pages = {529-533}, type = {Journal Article}, issn = {0028-0836}, author = {Mnih, Volodymyr and Kavukcuoglu, Koray and Silver, David and Rusu, Andrei A and Veness, Joel and Bellemare, Marc G and Graves, Alex and Riedmiller, Martin and Fidjeland, Andreas K and Ostrovski, Georg} } @article {2015, title = {Research Priorities for Robust and Beneficial Artificial Intelligence}, journal = {Association for the Advancement of Artificial Intelligence}, volume = {4}, year = {2015}, pages = {105-114}, abstract = {

Artificial intelligence (AI) research has explored a variety of problems and approaches since its inception, but for the last 20 years or so has been focused on the problems surrounding the construction of intelligent agents - systems that perceive and act in some environment. In this context, the criterion for intelligence is related to statistical and economic notions of rationality - colloquially, the ability to make good decisions, plans, or inferences. The adoption of probabilistic representations and statistical learning methods can led to a large degree of integration and crossfertilization between AI, machine learning, control theory, neuroscience, and other fields. The establishment of shared theoretical frameworks, combined with the availability of data and processing power, has yielded remarkable successes in various component tasks such as speech recognition, image classification, autonomous vehicles, machine translation, legged locomotion, and question-answering systems.

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