@conference {9999,
	title = {Project archetypes: A blessing and a curse for AI development},
	booktitle = {International Conference on Information Systems (ICIS)},
	year = {2022},
	address = {Copenhagen, Denmark},
	abstract = {<p>Software projects rely on what we propose to call project archetypes, i.e., pre-existing mental images of how projects work. They guide distribution of responsibilities, planning, or expectations. However, with the technological progress, project archetypes may become outdated, ineffective, or counterproductive by impeding more adequate approaches. Understanding archetypes of software development projects is core to leverage their potential. The development of applications using machine learning and artificial intelligence provides a context in which existing archetypes might outdate and need to be questioned, adapted, or replaced. We analyzed 36 interviews from 21 projects between IBM Watson and client companies and identified four project archetypes members initially used to understand the projects. We then derive a new project archetype, cognitive computing project, from the interviews. It can inform future development projects based on AI-development platforms. Project leaders should pro-actively manage project archetypes while researchers should investigate what guides initial understandings of software projects.</p>},
	url = {https://aisel.aisnet.org/icis2022/is_design/is_design/6 },
	attachments = {https://waim.network/sites/crowston.syr.edu/files/icis_archetypes_rev1_v20_zora.pdf},
	author = {Mateusz Dolata and Kevin Crowston and Gerhard Schwabe}
}
@article {2021,
	title = {Hybrid intelligence in business networks},
	journal = {Electronic Markets},
	year = {2021},
	month = {Nov-06-2021},
	issn = {1019-6781},
	doi = {10.1007/s12525-021-00481-4},
	attachments = {https://waim.network/sites/crowston.syr.edu/files/Ebel2021_Article_HybridIntelligenceInBusinessNe.pdf},
	author = {Ebel, Philipp and S{\"o}llner, Matthias and Leimeister, Jan Marco and Crowston, Kevin and de Vreede, Gert-Jan}
}
@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}
}
@conference {Sushina2020,
	title = {Artificial Intelligence in the Criminal Justice System: Leading Trends and Possibilities},
	booktitle = {Proceedings of the 6th International Conference on Social, economic, and academic leadership (ICSEAL-6-2019)},
	volume = {441},
	year = {2020},
	pages = {432{\textendash}437},
	publisher = {Atlantis Press},
	organization = {Atlantis Press},
	address = {Paris, France},
	keywords = {artificial intelligence, criminal justice system, digital technologies, Leadership},
	isbn = {978-94-6252-974-8},
	doi = {10.2991/assehr.k.200526.062},
	url = {https://www.atlantis-press.com/article/125940991},
	author = {Sushina, Tatyana and Sobenin, Andrew}
}
@article {Bessen2020,
	title = {Automation: A Guide for Policymakers},
	year = {2020},
	institution = {Brookings Institute},
	abstract = {<p>\ldots evidence that automation is causing mass unemployment, perhaps things are about to change. Some people, such as Martin Ford (2015), argue that {\textquotedblleft}this time is different.{\textquotedblright} Perhaps the rate of technological change is much more rapid with new artificial intelligence technologies \ldots</p>},
	url = {https://www.brookings.edu/research/automation-a-guide-for-policymakers/},
	author = {Bessen, James and Goos, Maarten and Salomons, Anna and van den Berge, Wiljan}
}
@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 {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}
}
@conference {Gerber2020,
	title = {Conceptualization of the Human-Machine Symbiosis {\textendash} A Literature Review},
	booktitle = {Proceedings of the 53rd Hawaii International Conference on System Sciences},
	volume = {3},
	year = {2020},
	pages = {289{\textendash}298},
	abstract = {The vision of a symbiotic partnership between humans and machines has existed since the 1960s. With this paper we provide the first conceptualization of the human-machine symbiosis (HMS) and make three important contributions: we present the fundamentals of HMS by focusing on objectives, requirements, and boundaries; we propose a framework for the design of HMS; and we review HMS research and, specifically, what the literature says with respect to whether HMS has already been achieved.},
	isbn = {9780998133133},
	doi = {10.24251/hicss.2020.036},
	author = {Gerber, Alina and Derckx, Patrick and D{\"o}ppner, Daniel A and Schoder, Detlef}
}
@booklet {Suran2020,
	title = {Frameworks for collective intelligence: A systematic literature review},
	howpublished = {ACM Computing Surveys},
	volume = {53},
	number = {1},
	year = {2020},
	pages = {1{\textendash}36},
	abstract = {Over the last few years, Collective Intelligence (CI) platforms have become a vital resource for learning, problem solving, decision-making, and predictions. This rising interest in the topic has to led to the development of several models and frameworks available in published literature. Unfortunately, most of these models are built around domain-specific requirements, i.e., they are often based on the intuitions of their domain experts and developers. This has created a gap in our knowledge in the theoretical foundations of CI systems and models, in general. In this article, we attempt to fill this gap by conducting a systematic review of CI models and frameworks, identified from a collection of 9,418 scholarly articles published since 2000. Eventually, we contribute by aggregating the available knowledge from 12 CI models into one novel framework and present a generic model that describes CI systems irrespective of their domains. We add to the previously available CI models by providing a more granular view of how different components of CI systems interact. We evaluate the proposed model by examining it with respect to six popular, ongoing CI initiatives available on the Web.},
	keywords = {collective intelligence, Crowdsourcing, Human computer interaction, Systematic literature review, Web 2.0, Wisdom of crowds},
	issn = {15577341},
	doi = {10.1145/3368986},
	author = {Suran, Shweta and Pattanaik, Vishwajeet and Draheim, Dirk}
}
@conference {Azarova2020,
	title = {Key Advantages and Risks of Implementing Artificial Intelligence in the Activities of Professional Communicators},
	booktitle = {Proceedings of the 2020 IEEE Communication Strategies in Digital Society Seminar, ComSDS 2020},
	year = {2020},
	pages = {82{\textendash}86},
	abstract = {Nowadays we observe a wide penetration of digital technologies into communication experts practice. Literature review demonstrates a great interest of both technical and humanitarian scholars to this subject. The research results, obtained by opinion survey (young adults of 19-24 years old), illustrate possible pragmatic and ethical effects of AI applications as well as their advantages and risks.},
	keywords = {AI technologies risks, artificial intelligence, digital technologies, professional communicators},
	isbn = {9781728164410},
	doi = {10.1109/ComSDS49898.2020.9101238},
	author = {Azarova, Liudmila and Kudryavtseva, Maria and Sharakhina, Larisa}
}
@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 {Kernbach2020,
	title = {Machine learning-based clinical prediction modeling {\textendash} A practical guide for clinicians},
	journal = {Artificial Intelligence in Precision Health},
	number = {June},
	year = {2020},
	pages = {257{\textendash}278},
	publisher = {Elsevier Inc.},
	keywords = {Alzheimers disease detection, artificial intelligence, Convolutional neural networks, Deep neural networks, Ensemble machine learning methods},
	isbn = {9780128171332},
	doi = {10.1016/b978-0-12-817133-2.00011-2},
	url = {https://arxiv.org/abs/2006.15069v1 http://dx.doi.org/10.1016/B978-0-12-817133-2.00011-2},
	author = {Kernbach, Julius M. and Staartjes, Victor E.}
}
@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}
}
@article {Spencer2020,
	title = {No automation please, we{\textquoteright}re British: Technology and the prospects for work},
	journal = {Cambridge Journal of Regions, Economy and Society},
	volume = {13},
	number = {1},
	year = {2020},
	pages = {117{\textendash}134},
	abstract = {This article assesses the impact and probably limits of automation. It looks, in particular, at the case of the UK economy. The prospects for automation are seen as necessarily uncertain and potentially regressive in their effects, with technology likely to sustain a large number of low-quality jobs. The deep-seated problems of the UK economy-low-investment, low-productivity and low-real wages-are seen as key impediments to forms of automation that work for all in society. It is argued that, without wider institutional reform, the UK will be unable to reap the full potential of automation.},
	keywords = {automation, investment, robots, technology, work},
	issn = {17521386},
	doi = {10.1093/cjres/rsaa003},
	author = {Spencer, David and Slater, Gary}
}
@conference {Sampson2020,
	title = {Predicting Automation of Professional Jobs in Healthcare},
	booktitle = {Proceedings of the 53rd Hawaii International Conference on System Sciences},
	volume = {3},
	year = {2020},
	pages = {3529{\textendash}3537},
	isbn = {9780998133133},
	doi = {10.24251/hicss.2020.433},
	author = {Sampson, Scott}
}
@article {Arduengo2020,
	title = {The Robot Economy : Here It Comes},
	journal = {International Journal of Social Robotics},
	number = {July},
	year = {2020},
	publisher = {Springer Netherlands},
	keywords = {blockchain, Cloud robotics, Intelligent robots, IoRT, Robot economy},
	issn = {1875-4805},
	doi = {10.1007/s12369-020-00686-1},
	url = {https://doi.org/10.1007/s12369-020-00686-1},
	author = {Arduengo, Miguel and Sentis, Luis}
}
@conference {Schneider2020,
	title = {Similarity Learning Networks for Animal Individual Re-Identification-Beyond the Capabilities of a Human Observer},
	booktitle = {Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision Workshops, WACVW 2020},
	volume = {479},
	year = {2020},
	pages = {44{\textendash}52},
	abstract = {Deep learning has become the standard methodology to approach computer vision tasks when large amounts of labeled data are available. One area where traditional deep learning approaches fail to perform is one-shot learning tasks where a model must correctly classify a new category after seeing only one example. One such domain is animal re-identification, an application of computer vision which can be used globally as a method to automate species population estimates from camera trap images. Our work demonstrates both the application of similarity comparison networks to animal re-identification, as well as the capabilities of deep convolutional neural networks to generalize across domains. Few studies have considered animal re-identification methods across species. Here, we compare two similarity comparison methodologies: Siamese and Triplet-Loss, based on the AlexNet, VGG-19, DenseNet201, MobileNetV2, and InceptionV3 architectures considering mean average precision (mAP)@1 and mAP@5. We consider five data sets corresponding to five different species: Humans, chimpanzees, humpback whales, fruit flies, and Siberian tigers, each with their own unique set of challenges. We demonstrate that Triplet Loss outperformed its Siamese counterpart for all species. Without any species-specific modifications, our results demonstrate that similarity comparison networks can reach a performance level beyond that of humans for the task of animal re-identification. The ability for researchers to re-identify an animal individual upon re-encounter is fundamental for addressing a broad range of questions in the study of population dynamics and community/behavioural ecology. Our expectation is that similarity comparison networks are the beginning of a major trend that could stand to revolutionize animal re-identification from camera trap data.},
	isbn = {9781728171623},
	doi = {10.1109/WACVW50321.2020.9096925},
	author = {Schneider, Stefan and Taylor, Graham W. and Kremer, Stefan C.}
}
@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 = {AI-Based Digital Assistants},
	journal = {Business \& Information Systems Engineering},
	volume = {61},
	year = {2019},
	month = {Jan-08-2019},
	pages = {535 - 544},
	issn = {2363-7005},
	doi = {10.1007/s12599-019-00600-8},
	author = {Maedche, Alexander and Legner, Christine and Benlian, Alexander and Berger, Benedikt and Gimpel, Henner and Hess, Thomas and Hinz, Oliver and Morana, Stefan and S{\"o}llner, Matthias}
}
@article {2019,
	title = {Artificial Intelligence and Deep Learning: The Future of Medicine and Medical Practice},
	journal = { Journal of The Association of Physicians of India ■ Vol. 67 },
	year = {2019},
	month = {05/2019},
	url = {https://www.semanticscholar.org/paper/Artificial-Intelligence-and-Deep-Learning\%3A-The-of-Sanal-Paul/795dc72decfb12389bbf7076d82fef70421b9933},
	author = {Madhusudana Girija Sanal and Kolin Paul and Senthil Kumar and Nirmal Kumar Gangul}
}
@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}
}
@conference {2019,
	title = {Beyond Dyadic Interactions: Considering Chatbots as Community Members},
	booktitle = {Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems  - CHI {\textquoteright}19},
	year = {2019},
	pages = {1-13},
	publisher = {ACM Press},
	organization = {ACM Press},
	address = {Glasgow, Scotland UK},
	isbn = {9781450359702},
	doi = {10.1145/3290605},
	author = {Seering, Joseph and Luria, Michal and Kaufman, Geoff and Hammer, Jessica}
}
@article {2019,
	title = {Blame the boss, not the robot},
	journal = {New Labor Forum},
	volume = {28},
	year = {2019},
	month = {Oct-05-2020},
	pages = {66 - 69},
	issn = {1095-7960},
	doi = {10.1177/1095796019837947},
	author = {Smith, Zachary}
}
@article {2019,
	title = {Bots Coordinating Work in Open Source Software Projects},
	journal = {Computer},
	volume = {52},
	year = {2019},
	month = {Jan-09-2019},
	pages = {52 - 60},
	issn = {0018-9162},
	doi = {10.1109/MC.2018.2885970},
	author = {Hukal, Philipp and Berente, Nicholas and Germonprez, Matt and Schecter, Aaron}
}
@conference {2019,
	title = {A chatbot for supporting teams in the empathy map method},
	booktitle = {Hawaii International Conference on System SciencesProceedings of the 52nd Hawaii International Conference on System Sciences},
	year = {2019},
	publisher = {Hawaii International Conference on System Sciences},
	organization = {Hawaii International Conference on System Sciences},
	doi = {10.24251/HICSS.2019.029},
	author = {Bittner, Eva and Shoury, Omid}
}
@article {2019,
	title = {Comparing artificial intelligence algorithms to 157 German dermatologists: the melanoma classification benchmark},
	journal = {European Journal of Cancer},
	volume = {111},
	year = {2019},
	month = {Jan-04-2019},
	pages = {30 - 37},
	issn = {09598049},
	doi = {10.1016/j.ejca.2018.12.016},
	author = {Brinker, Titus J. and Hekler, Achim and Hauschild, Axel and Berking, Carola and Schilling, Bastian and Alexander Enk and Haferkamp, Sebastian and Karoglan, Ante and von Kalle, Christof and Weichenthal, Michael and Sattler, Elke and Schadendorf, Dirk and Gaiser, Maria R. and Klode, Joachim and Jochen Sven Utikal}
}
@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}
}
@book {2019,
	title = {Designing AI futures: A symbiotic vision},
	volume = {1083},
	year = {2019},
	pages = {3 - 18},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	isbn = {978-3-030-29742-8},
	issn = {1865-0929},
	doi = {10.1007/978-3-030-29743-5_1},
	author = {Gill, Karamjit S.},
	editor = {Kravets, Alla G. and Groumpos, Peter P. and Shcherbakov, Maxim and Kultsova, Marina}
}
@conference {2019,
	title = {Does automation influence career decisions among South African students?},
	booktitle = {the South African Institute of Computer Scientists and Information Technologists 2019Proceedings of the South African Institute of Computer Scientists and Information Technologists 2019 on ZZZ - SAICSIT {\textquoteright}19},
	year = {2019},
	pages = {1 - 10},
	publisher = {ACM Press},
	organization = {ACM Press},
	address = {Skukuza, South AfricaNew York, New York, USA},
	isbn = {9781450372657},
	doi = {10.1145/335110810.1145/3351108.3351137},
	author = {Mbilini, Sakhumzi N. and le Roux, Daniel B. and Parry, Douglas A.}
}
@article {2019,
	title = {Does the use of synchrony and artificial intelligence in video interviews affect interview ratings and applicant attitudes?},
	journal = {Computers in Human Behavior},
	volume = {98},
	year = {2019},
	month = {Jan-09-2019},
	pages = {93 - 101},
	issn = {07475632},
	doi = {10.1016/j.chb.2019.04.012},
	author = {Suen, Hung-Yue and Chen, Mavis Yi-Ching and Lu, Shih-Hao}
}
@book {2019,
	title = {The fourth industrial revolution: Trends and impacts on the world of work},
	year = {2019},
	pages = {177 - 194},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	isbn = {978-3-319-94531-6},
	doi = {10.1007/978-3-319-94532-3_115},
	author = {Kim, Sang Yun},
	editor = {McGrath, Simon and Mulder, Martin and Papier, Joy and Suart, Rebecca}
}
@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 = {Future of work initiatives promise lots of noise and lots of activity, but to what end?},
	year = {2019},
	url = {https://sloanreview.mit.edu/article/reframing-the-future-of-work/},
	author = {Jeff Schwartz and John Hagel III and Maggie Wooll and Kelly Monahan}
}
@article {2019,
	title = {Higher education in the age of Artificial Intelligence},
	journal = {Postdigital Science and Education},
	volume = {1},
	year = {2019},
	month = {Jan-04-2019},
	pages = {236 - 239},
	issn = {2524-485X},
	doi = {10.1007/s42438-018-0005-8},
	author = {Sudlow, Brian}
}
@conference {2019,
	title = {How May I Help You? {\textendash} State of the Art and Open Research Questions for Chatbots at the Digital Workplace},
	booktitle = {Hawaii International Conference on System SciencesProceedings of the 52nd Hawaii International Conference on System Sciences},
	year = {2019},
	publisher = {Hawaii International Conference on System Sciences},
	organization = {Hawaii International Conference on System Sciences},
	isbn = {978-0-9981331-2-6},
	doi = {10.24251/HICSS.2019.013},
	author = {Meyer von Wolff, Raphael and Hobert, Sebastian and Schumann, Matthias}
}
@article {2019,
	title = {How one scientist coped when AI beat him at his life{\textquoteright}s work},
	year = {2019},
	url = {https://www.vox.com/future-perfect/2019/2/15/18226493/deepmind-alphafold-artificial-intelligence-protein-folding},
	author = {Sigal Samuel}
}
@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 = {Hybrid Intelligence},
	journal = {Business \& Information Systems Engineering},
	volume = {61},
	year = {2019},
	month = {Jan-10-2019},
	pages = {637 - 643},
	issn = {2363-7005},
	doi = {10.1007/s12599-019-00595-2},
	author = {Dellermann, Dominik and Ebel, Philipp and S{\"o}llner, Matthias and Leimeister, Jan Marco}
}
@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 = {Machine learning concepts, concerns and opportunities for a pediatric radiologist},
	journal = {Pediatric Radiology},
	volume = {49},
	year = {2019},
	month = {Jan-04-2019},
	pages = {509 - 516},
	issn = {0301-0449},
	doi = {10.1007/s00247-018-4277-7},
	author = {Moore, Michael M. and Slonimsky, Einat and Long, Aaron D. and Sze, Raymond W. and Iyer, Ramesh S.}
}
@conference {2019,
	title = {Machine learning in Artificial Intelligence: Towards a common understanding},
	booktitle = {Hawaii International Conference on System SciencesProceedings of the 52nd Hawaii International Conference on System Sciences},
	year = {2019},
	publisher = {Hawaii International Conference on System Sciences},
	organization = {Hawaii International Conference on System Sciences},
	doi = {10.24251/HICSS.2019.630},
	author = {K{\"u}hl, Niklas and Goutier, Marc and Hirt, Robin and Satzger, Gerhard}
}
@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 = {Negotiated and reciprocal exchange structures in human-agent cooperation},
	journal = {Computers in Human Behavior},
	volume = {90},
	year = {2019},
	month = {Jan-01-2019},
	pages = {288 - 297},
	issn = {07475632},
	doi = {10.1016/j.chb.2018.08.012},
	author = {Chiou, Erin K. and Lee, John D. and Su, Tianshuo}
}
@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 = {The present and future of deep learning in radiology},
	journal = {European Journal of Radiology},
	volume = {114},
	year = {2019},
	month = {Jan-05-2019},
	pages = {14 - 24},
	issn = {0720048X},
	doi = {10.1016/j.ejrad.2019.02.038},
	url = {https://doi.org/10.1016/j.ejrad.2019.02.038},
	author = {Saba, Luca and Biswas, Mainak and Kuppili, Venkatanareshbabu and Cuadrado Godia, Elisa and Suri, Harman S. and Edla, Damodar Reddy and Omerzu, Toma{\v z} and Laird, John R. and Khanna, Narendra N. and Mavrogeni, Sophie and Protogerou, Athanasios and Sfikakis, Petros P. and Viswanathan, Vijay and Kitas, George D. and Nicolaides, Andrew and Gupta, Ajay and Suri, Jasjit S.}
}
@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}
}
@mastersthesis {2019,
	title = {Recruiters just wanna have AI?},
	year = {2019},
	edition = {Spring semester 2019},
	url = {https://liu.diva-portal.org/smash/get/diva2:1333711/FULLTEXT01.pdf},
	author = {Hannimari Savola and Bijona Troqe}
}
@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}
}
@article {2019,
	title = {Scaling up analogical innovation with crowds and AI},
	journal = {Proceedings of the National Academy of Sciences},
	volume = {116},
	year = {2019},
	pages = {1870-1877},
	issn = {0027-8424},
	doi = {10.1073/pnas.1807185116},
	author = {Kittur, Aniket and Yu, Lixiu and Hope, Tom and Chan, Joel and Lifshitz-Assaf, Hila and Gilon, Karni and Ng, Felicia and Kraut, Robert E. and Shahaf, Dafna}
}
@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 = {Towards a smart automated society: Cognitive technologies, knowledge production, and economic growth},
	journal = {Economics, Management, and Financial Markets},
	volume = {14},
	year = {2019},
	month = {Jan-01-2019},
	pages = {44},
	issn = {1842-3191},
	doi = {10.22381/EMFM14120195},
	author = {Mitchell Udell and Vojtech Stehel and Tomas Kliestik and Jana Kliestikova and Pavol Durana}
}
@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 = {Will machine learning end the viability of radiology as a thriving medical specialty?},
	journal = {The British Journal of Radiology},
	volume = {92},
	year = {2019},
	month = {Jan-02-2019},
	pages = {20180416},
	issn = {0007-1285},
	doi = {10.1259/bjr.20180416},
	author = {Chan, Stephen and Siegel, Eliot L}
}
@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.}
}
@article {2018,
	title = {AI and the Economy},
	year = {2018},
	institution = {National Bureau of Economic Research},
	address = {Cambridge, MA},
	doi = {10.3386/w24689},
	author = {Furman, Jason and Seamans, Robert}
}
@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.}
}
@mastersthesis {2018,
	title = {Archetypes of artificial intelligence utilization},
	year = {2018},
	note = {How companies create and capture value from a novel
business technology},
	url = {https://aaltodoc.aalto.fi/handle/123456789/33983},
	author = {Selim Saukkomaa}
}
@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 in the computer-age threatens human beings and working conditions at workplaces},
	journal = {Electronics Science Technology and Application},
	volume = {5},
	year = {2018},
	month = {Feb-08-2019},
	issn = {2251-2608},
	doi = {10.18686/esta.v5i3.76},
	author = {Saithibvongsa, Phothong and Yu, Jae Eon}
}
@article {2018,
	title = {Artificial intelligence, machine learning and health systems},
	journal = {Journal of Global Health},
	volume = {8},
	year = {2018},
	month = {Sep-10-2019},
	issn = {2047-2978},
	doi = {10.7189/jogh.08.020303},
	author = {Panch, Trishan and Szolovits, Peter and Atun, Rifat}
}
@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 = {Is automation labor-displacing? Productivity growth, employment, and the labor share},
	year = {2018},
	institution = {National Bureau of Economic Research},
	address = {Cambridge, MA},
	doi = {10.3386/w24871},
	author = {Autor, David and Salomons, Anna}
}
@article {seidel2018autonomous,
	title = {Autonomous tools and design: a triple-loop approach to human-machine learning},
	journal = {Communications of the ACM},
	volume = {62},
	year = {2018},
	pages = {50{\textendash}57},
	publisher = {ACM},
	author = {Seidel, Stefan and Berente, Nicholas and Lindberg, Aron and Lyytinen, Kalle and Jeffrey V Nickerson}
}
@article {seidel2018autonomous,
	title = {Autonomous Tools in System Design: Reflective Practice in Ubisofts Ghost Recon Wildlands Project},
	journal = {Computer},
	volume = {51},
	year = {2018},
	pages = {16{\textendash}23},
	publisher = {IEEE},
	author = {Seidel, Stefan and Berente, Nicholas and Martinez, Beno\^{\i}t and Lindberg, Aron and Lyytinen, Kalle and Jeffrey V Nickerson}
}
@article {2018,
	title = {Chat bots are the new HR managers},
	journal = {Strategic HR Review},
	volume = {17},
	year = {2018},
	month = {Nov-06-2018},
	pages = {162 - 163},
	issn = {1475-4398},
	doi = {10.1108/SHR-03-2018-0024},
	author = {Sheth, Beerud}
}
@article {2018,
	title = {Cognitive and technological aspects of e-learning in context of robotization},
	journal = {Cognitive Science {\textendash} New Media {\textendash} Education},
	volume = {3},
	year = {2018},
	month = {Mar-06-2020},
	pages = {65},
	abstract = {The development of e-learning education led to the emergence of two main problems of such form of education, which are the user-system interaction from the cognitive prospective, and the analysis of massive data received out of students{\textquoteright} activity.  The development of artificial intelligence concepts and robotic process automation (RPA) tools, both problems might be solved in a more efficient way. The article presents the intelligent system of feedbacks, realized as JavaScript extension to Moodle platform, which intends to strength cognitive output of the preformed learning activity, creating an illusion of the trainer{\textquoteright}s presence and, therefore, contribute to the resolution of the first problem. The resolution of the second problem is proposed using artificial intelligence worker built in Blue Prism RPA platform, which performs validation of test questions upon strict criteria of selection. Such validation process allows to select the questions which are coherent with the index of complexity and the index of differentiation capacity.},
	keywords = {artificial intelligence worker, Blue Prism automation, intelligent system of feedbacks, robotic process automation},
	issn = {2543-506X},
	doi = {10.12775/CSNME.2017.013},
	url = {http://apcz.umk.pl/czasopisma/index.php/CSNME/article/view/CSNME.2017.013},
	author = {Shvets, Anna and Shvets, Valentyna}
}
@article {2018,
	title = {A critical approach to human helping in information systems: Heteromation in the Brazilian correspondent banking system},
	journal = {Information and Organization},
	volume = {28},
	year = {2018},
	month = {Jan-09-2018},
	pages = {111 - 128},
	issn = {14717727},
	doi = {10.1016/j.infoandorg.2018.08.002},
	author = {Diane E. Bailey and Eduardo H. Diniz and Bonnie A. Nardi and Paul M. Leonardi and Dan Sholler}
}
@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 = {Cryptocurrency, Artificial Intelligence and basic income as innovative technological system},
	journal = {Path of Science},
	volume = {4},
	year = {2018},
	month = {Jul-08-2020},
	pages = {2024 - 2030},
	doi = {10.22178/pos10.22178/pos.37-6},
	author = {Sopilnyk, Lyubomyr and Shevchuk, Andriy and Kopytko, Vasyl}
}
@article {2018,
	title = {Developing an ethical framework},
	journal = {Business Horizons},
	volume = {61},
	year = {2018},
	month = {Jan-11-2018},
	pages = {823 - 832},
	issn = {00076813},
	doi = {10.1016/j.bushor.2018.07.001},
	author = {Wright, Scott A. and Schultz, Ainslie E.}
}
@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.}
}
@article {2018,
	title = {Digital economy: backgrounds, main drivers and new challenges},
	journal = {SHS Web of Conferences},
	volume = {44},
	year = {2018},
	month = {Jan-01-2018},
	pages = {00006},
	doi = {10.1051/shsconf/20184400006},
	author = {Akaev, Askar and Sarygulov, Askar and Sokolov, Valentin},
	editor = {Sarygulov, A. and Sergeev, V. and Ungv{\'a}ri, L. and Semmler, W.}
}
@article {2018,
	title = {The economics of artificial intelligence: Implications for the future of work},
	year = {2018},
	isbn = {978-92-2-031137-0},
	issn = {978-92-2-031138-7},
	url = {https://www.ilo.org/wcmsp5/groups/public/---dgreports/---cabinet/documents/publication/wcms_647306.pdf},
	author = {Ekkehard Ernst and Rossana Merola and Daniel Samaan}
}
@mastersthesis {2018,
	title = {Economies of the future: Robots and Artificial Intelligence, the new economic motor or downfall of the working class?},
	year = {2018},
	month = {11/2018},
	type = {master thesis},
	abstract = {Technological progress and innovation have significantly contributed to global economic growth, societal advancement, and higher living standards. However, there is growing concern over the future that lies ahead because of increasing robotisation and progress in artificial intelligence (AI), which are feared to cause significant loss of labour demand. The body of posterior economic scientific work addressing this topic mostly concludes on a positive note. Namely, recent technological advancements have resulted in a net increase in labour demand, but this demand is redistributed to different tasks and occupations. Yet, future oriented research, most notably by Frey and Osborne (2017), has sparked a debate on the future of work due to estimations that indicate that over 40\% of jobs will become automatable in the next 20 years. Therefore, the societal question remains: Robots and Artificial Intelligence, the new economic motor or downfall of the working class?},
	keywords = {economics of automation},
	url = {https://repository.tudelft.nl/islandora/object/uuid\%3Af30b5d2e-d48d-4c90-8780-3d55a2e1a7d3},
	author = {Koen Spaanderman}
}
@article {2018,
	title = {The effects of automation},
	year = {2018},
	url = {https://www.dropbox.com/sh/8sxki5mwikjxlte/AAB6zvW5F0s2FIJ6jUmEW7P-a/Popular\%20press\%20about\%20impacts?dl=0\&preview=effects+of+automation.pdf\&subfolder_nav_tracking=1},
	author = {Jim L. Smith}
}
@article {2018,
	title = {The evolving role of ICT in the economy},
	year = {2018},
	month = {06/2018},
	keywords = {consulting reports},
	url = {http://www.lse.ac.uk/business-and-consultancy/consulting/consulting-reports/the-evolving-role-of-ict-in-the-economy},
	author = {Mirko Draca and Ralf Martin and Rosa Sanchis-Guarner}
}
@article {2018,
	title = {Experimental evidence on complimentarities between human capital and machine learning},
	year = {2018},
	month = {01/2018},
	keywords = {economics of automation},
	url = {https://hbswk.hbs.edu/item/different-strokes-for-different-folks-experimental-evidence-on-complementarities-between-human-capital-and-machine-learning},
	author = {Prithwiraj Choudhury and Evan Starr and Rajshree Agarwal}
}
@article {2018,
	title = {Experimental evidence on productivity complementarities},
	number = {18-065},
	year = {2018},
	publisher = {Harvard Business School Working Paper},
	keywords = {Human Capital, Performance Productivity, Technological Innovation, Technology Adoption},
	url = {https://www.hbs.edu/faculty/Pages/item.aspx?num=53855},
	author = {Prithwiraj Choudhury}
}
@conference {2018,
	title = {A framework for understanding chatbots and their future},
	booktitle = {the 11th International WorkshopProceedings of the 11th International Workshop on Cooperative and Human Aspects of Software Engineering  - CHASE {\textquoteright}18},
	year = {2018},
	pages = {13 - 16},
	publisher = {ACM Press},
	organization = {ACM Press},
	address = {Gothenburg, SwedenNew York, New York, USA},
	isbn = {9781450357258},
	doi = {10.1145/3195836.3195859},
	author = {Paikari, Elahe and van der Hoek, Andr{\'e}}
}
@article {2018,
	title = {Google glass is back with AI},
	year = {2018},
	month = {07/2018},
	abstract = {An app for Glass aimed at factory workers can understand spoken language and respond verbally.},
	url = {https://www.wired.com/story/google-glass-is-backnow-with-artificial-intelligence/},
	author = {Tom Simonite}
}
@article {2018,
	title = {High-skilled white-collar work? Machines can do that, too},
	year = {2018},
	url = {https://www.nytimes.com/2018/07/07/business/economy/algorithm-fashion-jobs.html},
	author = {Noam Scheiber}
}
@article {2018,
	title = {How does the digital transformation affect organizations? Key themes of change in work design and leadership},
	journal = {management revu},
	volume = {29},
	year = {2018},
	month = {Jan-01-2018},
	pages = {114 - 138},
	issn = {0935-9915},
	doi = {10.5771/0935-9915-2018-2-114},
	author = {Schwarzm{\"u}ller, Tanja and Brosi, Prisca and Duman, Denis and Welpe, Isabell M.}
}
@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 = {Human and smart machine co-learning with brain computer interface},
	journal = {IEEE SMC Magazine},
	volume = {4},
	year = {2018},
	keywords = {human and machine inttelligence},
	url = {https://arxiv.org/abs/1802.06521},
	author = {Chang - Shing Lee and Mei - Hui Wang and Li - Wei Ko and Naoyuki Kubota and Lu - An Lin and Shinya Kitaoka and Yu - Te Wang and Shun - Feng Su}
}
@article {2018,
	title = {The impact of automation on employment: Just the usual structural change? },
	journal = {Sustainability},
	volume = {10},
	year = {2018},
	month = {Jan-05-2018},
	pages = {1661},
	doi = {10.3390/su10051661},
	author = {Vermeulen, Ben and Kesselhut, Jan and Pyka, Andreas and Saviotti, Pier}
}
@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.}
}
@proceedings {2018,
	title = {A literature Analysis of Research on Artificial Intelligence in Management Information System (MIS)},
	year = {2018},
	abstract = {This article presents an overview of Artificial Intelligence (AI) research applied in the Management Information System field by analyzing the databases of the AIS journal basket list representing the major international journal on MIS. AN initial set of 438 published papers was identified based on our search criteria. From those, 74 were selected because of their appropriate fit with the research questions. They were organized into domains, type of research, data collection strategy and AI techniques. We found; (1) MIS Quarterly published 42\% of the papers; (2) 50\% of studies were on Information Systems applications, followed by 15\% on Knowledge Management; and (3) 7 AI techniques were presented. This paper provides an overview of applied AI in Management, highlights the most studies related topics and techniques and contributes to a research agenda on the subject.},
	keywords = {artificial intelligence, Information Systems, Literature Analysis},
	author = {Alexandre Moreira Nascimento and Maria Alexandra V. C. da Cunha and Fernando de Souza Meirelles and Eusebio Scornavacca and Vinicius Veloso de Melo}
}
@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}
}
@article {2018,
	title = {Machine learning, social learning and the governance of self-driving cars},
	journal = {Social Studies of Science},
	volume = {48},
	year = {2018},
	pages = {25-56},
	abstract = {Self-driving cars, a quintessentially {\textquoteleft}smart{\textquoteright} technology, are not born smart. The algorithms that control their movements are learning as the technology emerges. Self-driving cars represent a high-stakes test of the powers of machine learning, as well as a test case for social learning in technology governance. Society is learning about the technology while the technology learns about society. Understanding and governing the politics of this technology means asking {\textquoteleft}Who is learning, what are they learning and how are they learning?{\textquoteright} Focusing on the successes and failures of social learning around the much-publicized crash of a Tesla Model S in 2016, I argue that trajectories and rhetorics of machine learning in transport pose a substantial governance challenge. {\textquoteleft}Self-driving{\textquoteright} or {\textquoteleft}autonomous{\textquoteright} cars are misnamed. As with other technologies, they are shaped by assumptions about social needs, solvable problems, and economic opportunities. Governing these technologies in the public interest means improving social learning by constructively engaging with the contingencies of machine learning.},
	doi = {10.1177/0306312717741687},
	author = {Jack Stilgoe}
}
@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 = {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 nordic future of work drivers, institutions, and politics},
	year = {2018},
	institution = {Nordic Council of Ministers},
	url = {https://norden.diva-portal.org/smash/get/diva2:1265618/FULLTEXT01.pdf},
	author = {Jon Erik D{\o}lvik and Johan R{\o}ed Steen}
}
@article {2018,
	title = {Occupational Classifications With A Machine Learning Approach},
	year = {2018},
	keywords = {administrative data, machine learning, occupational classifications, transaction data, UMETRICS},
	url = {https://www.iza.org/publications/dp/11738/occupational-classifications-a-machine-learning-approach},
	author = {Akina Ikudo and Julia Lane and Joseph Staudt and Bruce A. Weinberg}
}
@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}
}
@article {2018,
	title = {Skill shift automation and the future of the workforce},
	year = {2018},
	month = {05/2018},
	url = {https://www.mckinsey.com/featured-insights/future-of-work/skill-shift-automation-and-the-future-of-the-workforce},
	author = {Jaccques Bughin and Eric Hazan and Susan Lund and Peter Dahlstrom and Anna Wiesinger and Amresh Subramaniam}
}
@article {2018,
	title = {Social robots as cultural objects: The sixth dimension of dynamicity?},
	journal = {The Information Society},
	volume = {34},
	year = {2018},
	month = {Mar-05-2020},
	pages = {141 - 152},
	issn = {0197-2243},
	doi = {10.1080/01972243.2018.1444253},
	author = {Fortunati, Leopoldina and Sarrica, Mauro and Ferrin, Giovanni and Brondi, Sonia and Honsell, Furio}
}
@article {2018,
	title = {Sustainable skills for the world of work in the digital age},
	journal = {Systems Research and Behavioral Science},
	volume = {35},
	year = {2018},
	month = {Jan-07-2018},
	pages = {399 - 405},
	doi = {10.1002/sres.v35.410.1002/sres.2540},
	author = {Sousa, Maria Jos{\'e} and Wilks, Daniela},
	editor = {Laszlo, Alexander}
}
@conference {2018,
	title = {Towards an understanding of how chatbots create value},
	booktitle = {Hawaii International Conference on System SciencesProceedings of the 51st 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.2018.255},
	author = {Stoeckli, Emanuel and Uebernickel, Falk and Brenner, Walter}
}
@article {2018,
	title = {Transparency and fairness in machine learning applications},
	journal = {Texas A\&M Journal of Property Law},
	volume = {4},
	year = {2018},
	url = {https://scholarship.law.tamu.edu/cgi/viewcontent.cgi?article=1079\&context=journal-of-property-law},
	author = {Jim Shook and Robyn Smith and Alex Antonio}
}
@article {2018,
	title = {When Will AI Exceed Human Performance? Evidence from AI Experts},
	year = {2018},
	url = {https://jair.org/index.php/jair/article/download/11222/26431/},
	author = {Katja Grace and John Salvatier and Allan Dafoe and Baobao Zhang and Owain Evans}
}
@article {2018,
	title = {Working and organizing in the age of the learning algorithm},
	journal = {Information and Organization},
	volume = {28},
	year = {2018},
	publisher = {Elsevier},
	chapter = {62-70},
	abstract = {Learning algorithms, technologies that generate responses, classifications, or dynamic predictions that resemble those of a knowledge worker, raise important research questions for organizational scholars related to work and organizing. We suggest that such algorithms are distinguished by four consequential aspects: black-boxed performance, comprehensive digitization, anticipatory quantification, and hidden politics. These aspects are likely to alter work and organizing in qualitatively different ways beyond simply signaling an acceleration of long-term technology trends. Our analysis indicates that learning algorithms will transform expertise in organizations, reshape work and occupational boundaries, and offer novel forms of coordination and control. Thus, learning algorithms can be considered performative due to the extent to which their use can shape and alter work and organizational realities. Their rapid deployment requires scholarly attention to societal issues such as extent to which the algorithm is authorized to make decisions, the need to incorporate morality in the technology, and their digital iron-cage potential.},
	doi = {10.1016/j.inforandorg.2018.02.005},
	author = {Samer Faraj and Stella Pachidi and Karla Sayegh}
}
@article {2018,
	title = {Working and organizing in the age of the learning algorithm},
	journal = {Information and Organization},
	volume = {28},
	year = {2018},
	month = {Jan-03-2018},
	pages = {62 - 70},
	issn = {14717727},
	doi = {10.1016/j.infoandorg.2018.02.005},
	author = {Faraj, Samer and Pachidi, Stella and Sayegh, Karla}
}
@article {2017,
	title = {AI in Action: Machines that make sense of the sky},
	journal = {Science},
	volume = {357},
	year = {2017},
	month = {Jul-07-2017},
	pages = {26 - 26},
	issn = {0036-8075},
	doi = {10.1126/science.357.6346.26},
	author = {Sokol, Joshua}
}
@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 For Social Good},
	year = {2017},
	note = {Sponsored by the Computing Community Consortium (CCC)},
	url = {https://cra.org/ccc/wp-content/uploads/sites/2/2016/04/AI-for-Social-Good-Workshop-Report.pdf},
	author = {Gregory D. Hager and Ann Drobnis and Fei Fang and Rayid Ghani and Amy Greenwald and Terah Lyons and David C. Parkes and Jason Schultz and Suchi Saria and Stephen F. Smith and Milind Tambe}
}
@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 = {Artificial Intelligence Index},
	year = {2017},
	url = {https://aiindex.org/2017-report.pdf},
	author = {Yoav Shoham and Raymond Perrault and Erik Brynjolfsson and Jack Clark}
}
@article {2017,
	title = {Assessing drivers{\textquoteright} response during automated driver support system failures with non-driving tasks},
	journal = {Journal of Safety Research},
	volume = {61},
	year = {2017},
	month = {Jan-06-2017},
	pages = {149 - 155},
	issn = {00224375},
	doi = {10.1016/j.jsr.2017.02.009},
	author = {Shen, Sijun and Neyens, David M.}
}
@article {2017,
	title = {Big data and organizational design {\textendash} the brave new world of algorithmic management and computer augmented transparency},
	journal = {Innovation},
	volume = {19},
	year = {2017},
	month = {Feb-01-2017},
	pages = {23 - 30},
	issn = {1447-9338},
	doi = {10.1080/14479338.2016.1252043},
	author = {Schildt, Henri}
}
@article {2017,
	title = {A deep learning approach for quantifying tumor extent},
	journal = {Scientific Reports},
	volume = {7},
	year = {2017},
	month = {Jan-06-2017},
	doi = {10.1038/srep46450},
	author = {Cruz-Roa, Angel and Gilmore, Hannah and Basavanhally, Ajay and Feldman, Michael and Ganesan, Shridar and Shih, Natalie N.C. and Tomaszewski, John and Gonz{\'a}lez, Fabio A. and Madabhushi, Anant}
}
@article {2017,
	title = {DeepMind just published a mind blowing paper: PathNet},
	year = {2017},
	url = {https://medium.com/mcgill-artificial-intelligence-review/deepmind-just-published-a-mind-blowing-paper-pathnet-f72b1ed38d46},
	author = {Th{\'e}o Szymkowiak}
}
@article {RN30632,
	title = {Dermatologist-level classification of skin cancer with deep neural networks},
	journal = {Nature},
	volume = {542},
	number = {7639},
	year = {2017},
	pages = {115{\textendash}118},
	type = {Journal Article},
	issn = {0028-0836},
	author = {Esteva, Andre and Kuprel, Brett and Novoa, Roberto A and Ko, Justin and Swetter, Susan M and Blau, Helen M and Thrun, Sebastian}
}
@article {2017,
	title = {Empowerment As Replacement for the Three Laws of Robotics},
	journal = {Frontiers in Robotics and AI},
	volume = {4},
	year = {2017},
	month = {May-06-2019},
	doi = {10.3389/frobt.2017.00025},
	author = {Salge, Christoph and Polani, Daniel}
}
@article {2017,
	title = {An experimental comparison of Chatbot and Human task partners},
	journal = {Computers in Human Behavior},
	volume = {75},
	year = {2017},
	month = {Jan-10-2017},
	pages = {461 - 468},
	issn = {07475632},
	doi = {10.1016/j.chb.2017.05.045},
	author = {Fryer, Luke K. and Ainley, Mary and Thompson, Andrew and Gibson, Aaron and Sherlock, Zelinda}
}
@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}
}
@conference {RN30639,
	title = {Huddler: Convening stable and familiar crowd teams despite unpredictable availability},
	booktitle = {Proceedings of the Conference on Computer supported Cooperative Work and Social Media},
	year = {2017},
	type = {Conference Proceedings},
	author = {Salehi, Niloufar and McCabe, Andrew and Valentine, Melissa and Michael S. Bernstein}
}
@article {2017,
	title = {Jobs lost, jobs gained: Workforce transactions in a time of automation},
	year = {2017},
	keywords = {consulting reports},
	url = {https://www.mckinsey.com/~/media/mckinsey/featured\%20insights/future\%20of\%20organizations/what\%20the\%20future\%20of\%20work\%20will\%20mean\%20for\%20jobs\%20skills\%20and\%20wages/mgi-jobs-lost-jobs-gained-report-december-6-2017.ashx},
	author = {James Manyika and Susan Lund and Michael Chui and Jonathan Woetzel and Ryan Ko and Saurabh Sanghvi and Parul Batra and Jacques Bughin}
}
@article {2017,
	title = {A new chart conclusively proves that automation is a serious threat},
	year = {2017},
	month = {11/2017},
	type = {Robots \& Machines},
	url = {https://futurism.com/new-chart-proves-automation-serious-threat},
	author = {Scott Santens}
}
@article {2017,
	title = {Seeing Like a Tesla: How Can We Anticipate Self-Driving Worlds?},
	journal = {Glocalism},
	volume = {3},
	year = {2017},
	abstract = {In the last five years, investment and innovation in self-driving cars has accelerated dramatically. Automotive autonomy, once seen as impossible, is now sold as inevitable. Much of the governance discussion has centred on risk: will the cars be safer than their human-controlled counterparts? As with conventional cars, harder long-term questions relate to the future worlds that self-driving technologies might enable or even demand. The vision of an autonomous vehicle {\textendash} able to navigate the world{\textquoteright}s complexity using only its sensors and processors {\textendash} on offer from companies like Tesla is intentionally misleading. So-called {\textquotedblleft}autonomous{\textquotedblright} vehicles will depend upon webs of social and technical connectivity. For their purported benefits to be realised, infrastructures that were designed around humans will need to be upgraded in order to become machine-readable. It is vital to anticipate the politics of self-driving worlds in order to avoid exacerbating the inequalities that have emerged around conventional cars. Rather than being dazzled by the Tesla view, policymakers should start seeing like a city, from multiple perspectives. Good governance for self-driving cars means democratising experimentation and creating genuine collaboration between companies and local governments.

 },
	keywords = {automotive autonomy, governance, risk, self-driving cars, Tesla},
	doi = {10.12893/gjcpi.2017.3.2},
	author = {Jack Stilgoe}
}
@article {2017,
	title = { A self-driving shuttle in Las Vegas got into an accident on its first day of service},
	year = {2017},
	month = {11/2017},
	keywords = {autonomous vehicles},
	url = {https://www.theverge.com/2017/11/8/16626224/las-vegas-self-driving-shuttle-crash-accident-first-day},
	author = {Nick Statt}
}
@article {2017,
	title = {Situational awareness, workload, and workflow preferences},
	journal = {The International Journal of Robotics Research},
	volume = {36},
	year = {2017},
	month = {Sep-06-2017},
	pages = {597 - 617},
	issn = {0278-3649},
	doi = {10.1177/0278364916688255},
	author = {Gombolay, Matthew and Bair, Anna and Huang, Cindy and Shah, Julie}
}
@article {2017,
	title = {Speculations and concerns on robots status in society},
	journal = {Journal of science communication},
	year = {2017},
	keywords = {bots, Participation and science governance, Public engagement with science and technology, Science and policy-making},
	url = {https://jcom.sissa.it/sites/default/files/documents/JCOM_1604_2017_C06.pdf},
	author = {Erik Stengler and Jimena Escudero Perez}
}
@article {2017,
	title = {The strategic opportunities and challenges of algorithmic decision-making},
	journal = {The Journal of Strategic Information Systems},
	volume = {26},
	year = {2017},
	month = {Jan-09-2017},
	pages = {185 - 190},
	issn = {09638687},
	doi = {10.1016/j.jsis.2017.08.002},
	author = {Galliers, R.D. and Newell, S. and Shanks, G. and Topi, H.}
}
@article {2017,
	title = {When you{\textquoteright}re not quite sure if your teacher is human},
	year = {2017},
	month = {05/2017},
	url = {https://www.npr.org/sections/ed/2017/05/08/524550295/when-youre-not-quite-sure-if-your-teacher-is-human},
	author = {Tasnim Shamma}
}
@article {2017,
	title = {Workforce of the future: The competing forces shaping 2030},
	year = {2017},
	url = {https://www.pwc.co.uk/economic-services/ukeo/pwc-uk-economic-outlook-full-report-march-2017-v2.pdf},
	author = {Carol Stubbings Stubbings and Jon Williams}
}
@article {2017,
	title = {Working algorithms: Software automation and the future of work},
	journal = {Work and Occupations},
	volume = {44},
	year = {2017},
	month = {Nov-11-2018},
	pages = {376 - 423},
	issn = {0730-8884},
	doi = {10.1177/0730888417726119},
	author = {Shestakofsky, Benjamin}
}
@article {2016,
	title = {ARTIFICIAL INTELLIGENCE AND LIFE IN 2030},
	year = {2016},
	url = {https://ai100.stanford.edu/sites/g/files/sbiybj9861/f/ai_100_report_0831fnl.pdf}
}
@article {2016,
	title = {Citizen science on a smartphone: Participants{\textquoteright} motivations and learning},
	journal = {Public Understanding of Science},
	volume = {25},
	year = {2016},
	month = {Mar-01-2017},
	pages = {45 - 60},
	issn = {0963-6625},
	doi = {10.1177/0963662515602406},
	author = {Land-Zandstra, Anne M. and Devilee, Jeroen L. A. and Snik, Frans and Buurmeijer, Franka and van den Broek, Jos M.}
}
@article {2016,
	title = {Differences in perceptions of communication quality between a Twitterbot and human agent for information seeking and learning},
	journal = {Computers in Human Behavior},
	volume = {65},
	year = {2016},
	month = {Jan-12-2016},
	pages = {666 - 671},
	issn = {07475632},
	doi = {10.1016/j.chb.2016.07.003},
	author = {Edwards, Chad and Beattie, Austin J. and Edwards, Autumn and Spence, Patric R.}
}
@conference {2016,
	title = {Disrupting developer productivity one bot at a time},
	booktitle = {the 2016 24th ACM SIGSOFT International SymposiumProceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering - FSE 2016},
	year = {2016},
	pages = {928 - 931},
	publisher = {ACM Press},
	organization = {ACM Press},
	address = {Seattle, WA, USANew York, New York, USA},
	isbn = {9781450342186},
	doi = {10.1145/2950290.2983989},
	author = {Storey, Margaret-Anne and Zagalsky, Alexey}
}
@mastersthesis {2016,
	title = {Exploring design principles for human-machine symbiosis: Insights from constructing an air transportation logistics artifact},
	year = {2016},
	keywords = {air transportation logistics, design science research, heuristic theorizing, huma n - machine symbiosis},
	url = {https://www.semanticscholar.org/paper/Artificial-Intelligence-and-its-Role-in-Near-Future-Shabbir-Anwer/b93d9995f9ce3b15f4c4855ae62f0bf6f9bc041f},
	author = {Daniel A. D{\"o}ppner and Detlef Schoder and Robert Wayne Gregory and Honorata Siejka}
}
@article {2016,
	title = {If you{\textquoteright}re not collecting productivity data, you{\textquoteright}ll never succeed at work},
	year = {2016},
	month = {02/2016},
	url = {https://hbr.org/2016/02/if-youre-not-collecting-productivity-data-youll-never-succeed-at-work},
	author = {Michael Schrage}
}
@article {2015,
	title = {Artificial Intelligence and its Role in Near Future},
	volume = {JOURNAL OF L A T E X CLASS FILES, VOL. 14, NO. 8, AUGUST 2015},
	year = {2015},
	url = {https://www.semanticscholar.org/paper/Artificial-Intelligence-and-its-Role-in-Near-Future-Shabbir-Anwer/b93d9995f9ce3b15f4c4855ae62f0bf6f9bc041f},
	author = {Jahanzaib Shabbir and Tarique Anwer}
}
@article {2015,
	title = {Decision-making authority, team efficiency and human worker satisfaction in mixed human{\textendash}robot teams},
	journal = {Autonomous Robots},
	volume = {39},
	year = {2015},
	month = {Jan-10-2015},
	pages = {293 - 312},
	issn = {0929-5593},
	doi = {10.1007/s10514-015-9457-9},
	author = {Gombolay, Matthew C. and Gutierrez, Reymundo A. and Clarke, Shanelle G. and Sturla, Giancarlo F. and Shah, Julie A.}
}
@article {2015,
	title = {An executive{\textquoteright}s guide to machine learning},
	journal = {McKinsey Quarterly},
	year = {2015},
	type = {Technology, Media \& Telecommunications},
	url = {https://www.mckinsey.com/industries/technology-media-and-telecommunications/our-insights/an-executives-guide-to-machine-learning},
	author = {Dorian Pyle and Cristina San Jose}
}
@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}
}
@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 {2015,
	title = {Norms, institutions and robots},
	journal = {Journal of latex class files},
	volume = {14},
	year = {2015},
	month = {08/2015},
	url = {https://arxiv.org/abs/1807.11456},
	author = {Stevan Tomic and Federico Pecora and Alessandro Saffotti}
}
@article {2014,
	title = {The impact of robotics on employment and motivation of employees in the service sector, with special reference to health care},
	journal = {Safety and Health at Work},
	volume = {5},
	year = {2014},
	month = {Jan-12-2014},
	pages = {198 - 202},
	issn = {20937911},
	doi = {10.1016/j.shaw.2014.07.003},
	author = {Qureshi, Mohammed Owais and Syed, Rumaiya Sajjad}
}
@article {2014,
	title = {Testing the differences in perceptions of communication quality for a human agent and a bot agent on Twitter},
	journal = {Computers in Human Behavior},
	volume = {33},
	year = {2014},
	month = {Jan-04-2014},
	pages = {372 - 376},
	issn = {07475632},
	doi = {10.1016/j.chb.2013.08.013},
	author = {Edwards, Chad and Edwards, Autumn and Spence, Patric R. and Shelton, Ashleigh K.}
}
@article {2014,
	title = {A transdisciplinary perspective on hedonomic sustainability design},
	year = {2014},
	keywords = {environmental efficacy, environmental sustainability, hedonomics, sustainable design, transdisciplinary research},
	doi = {10.1177\%2F1064804613516762},
	url = {https://journals.sagepub.com/doi/abs/10.1177/1064804613516762?journalCode=erga},
	author = {Stephen M. Fiore and Elizabeth Philips and Brittany C. Sellers}
}
@article {2013,
	title = {A Comprehensive Survey of Data Mining-based Fraud Detection Research},
	year = {2013},
	keywords = {adversarial detection, automated fraud detection, Data mining applications},
	url = {https://arxiv.org/ftp/arxiv/papers/1009/1009.6119.pdf},
	author = {Clifton Phua and Vincent Lee and Kate Smith and Ross Gayler}
}
@article {2013,
	title = {The convergence of engineering and the life sciences},
	journal = {National Academy of Engineering},
	volume = {43},
	year = {2013},
	month = {10/2013},
	url = {https://www.nae.edu/88364/The-Convergence-of-Engineering-and-the-Life-Sciences},
	author = {Pjilip A. Sharp and Robert Langer}
}
@article {2013,
	title = {Ethical algorithms},
	year = {2013},
	url = {https://www.sharrockandanderson.co.uk/wp-content/uploads/2017/04/Ethical-Algorithms.pdf},
	author = {R.J. Anderson and W.W. Sharrock}
}
@conference {kittur2013future,
	title = {The future of crowd work},
	booktitle = {Proceedings of the 2013 conference on Computer supported cooperative work},
	year = {2013},
	pages = {1301{\textendash}1318},
	publisher = {ACM},
	organization = {ACM},
	author = {Kittur, Aniket and Jeffrey V Nickerson and Michael S. Bernstein and Gerber, Elizabeth and Shaw, Aaron and Zimmerman, John and Lease, Matt and Horton, John}
}
@article {2013,
	title = {The great reversal in the demand for skill and cognitive tasks},
	year = {2013},
	institution = {National Bureau of Economic Research},
	address = {Cambridge, MA},
	doi = {10.3386/w18901},
	author = {Beaudry, Paul and Green, David and Sand, Benjamin}
}
@article {2013,
	title = {Quantifying the interdisciplinarity of scientific journals and fields},
	journal = {Journal of Informetrics},
	volume = {7},
	year = {2013},
	month = {Jan-04-2013},
	pages = {469 - 477},
	issn = {17511577},
	doi = {10.1016/j.joi.2013.01.007},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S1751157713000096https://api.elsevier.com/content/article/PII:S1751157713000096?httpAccept=text/xmlhttps://api.elsevier.com/content/article/PII:S1751157713000096?httpAccept=text/plain},
	author = {Silva, F.N. and Rodrigues, F.A. and Oliveira, O.N. and da F. Costa, L.}
}
@article {2013,
	title = {The rise and fall of interdisciplinary research: The case of open source innovation},
	journal = {Research Policy},
	volume = {42},
	year = {2013},
	month = {Jan-06-2013},
	pages = {1138 - 1151},
	issn = {00487333},
	doi = {10.1016/j.respol.2013.01.010},
	author = {Raasch, Christina and Lee, Viktor and Spaeth, Sebastian and Herstatt, Cornelius}
}
@conference {2013,
	title = {Survey of metrics for human-robot interaction},
	booktitle = {IEEE International Conference},
	year = {2013},
	keywords = {bots},
	url = {https://ieeexplore.ieee.org/document/6483569},
	author = {Robin R. Murphy and Debra Schreckenghost}
}
@article {2010,
	title = {Prediction modeling using EHR data: Challenges, strategies, and a comparison of machine learning approaches},
	journal = {Medical Care},
	volume = {48},
	year = {2010},
	publisher = {Lippincott Williams \& Wilkins},
	type = {Emerging Methods and Policy Applications},
	url = {https://www.jstor.org/stable/20720782},
	author = {Jionglin Wu and Jason Ray and Walter F. Stewart}
}
@conference {2010,
	title = {Using machine learning for network intrusion detection},
	year = {2010},
	note = {Proceedings of the IEEE Symposium on Security and Privacy 2010, Oakland, California, pp. 305-316},
	url = {http://www.icsi.berkeley.edu/pubs/networking/outsidethe10.pdf},
	author = {R. Sommer and V. Paxson}
}
@conference {2009,
	title = {The changing face of digital science},
	booktitle = {the 27th international conference extended abstractsProceedings of the 27th international conference extended abstracts on Human factors in computing systems - CHI EA {\textquoteright}09},
	year = {2009},
	pages = {4819},
	publisher = {ACM Press},
	organization = {ACM Press},
	address = {Boston, MA, USANew York, New York, USA},
	isbn = {9781605582474},
	doi = {10.1145/1520340.1520749},
	author = {Aragon, Cecilia R. and Poon, Sarah and Silva, Claudio T.}
}
@article {2007,
	title = {Automation-induced complacency for monitoring highly reliable systems: the role of task complexity, system experience, and operator trust},
	journal = {Theoretical Issues in Ergonomics Science},
	volume = {8},
	year = {2007},
	month = {Jan-07-2007},
	pages = {321 - 348},
	issn = {1463-922X},
	doi = {10.1080/14639220500535301},
	author = {Bailey, N. R. and Scerbo, M. W.}
}
@article {2006,
	title = {Literature review of teamwork models},
	number = {CMU-RI-TR-06-50},
	year = {2006},
	institution = {Robotics Institute, Carnegie Mellon University},
	url = {https://www.researchgate.net/publication/246704657_Literature_Review_of_Teamwork_Models},
	author = {Katia Sycara and Gita Sukthankar}
}
@article {2006,
	title = {Technical change, job tasks, and rising educational demands: looking outside the wage structure},
	journal = {Journal of Labor Economics},
	volume = {24},
	year = {2006},
	month = {Jan-04-2006},
	pages = {235 - 270},
	issn = {0734-306X},
	doi = {10.1086/499972},
	author = {Spitz-Oener, Alexandra}
}
@inbook {RN30398,
	title = {Automation surprises},
	booktitle = {Handbook of Human Factors and Ergonomics},
	volume = {2},
	year = {1997},
	pages = {1926{\textendash}1943},
	type = {Book Section},
	author = {Sarter, Nadine B. and Woods, David D. and Billings, Charles E.}
}
@article {1994,
	title = {Design concepts of computer-aided integrated manufacturing systems: Work-psychological concepts and empirical findings},
	journal = {International Journal of Industrial Ergonomics},
	volume = {17},
	year = {1994},
	publisher = {Elsevier},
	chapter = {11-19},
	abstract = {The research project "GRIPS" is investigating the design of computer-aided integrated manufacturing systems from a work psychological perspective. The goal is to develop and empirically support adequate design concepts. The project consists of three phases. Evidence from a broad questionnaire survey indicates that most CIM implementations fail to meet expectations associated therewith. Based on the assumption that only the joint optimization of social and technical system results in humane working conditions and economic efficiency, implementations and use of CIM systems has been investigated in 60 companies in Switzerland. THe conceptual framework distinguishes technically-oriented and work-oriented design concepts on four levels; the enterprise, the organizational unit, the group and the individual. Work-oriented manufacturing systems - as opposed to technically-oriented ones - are characterized by decentralization, functional integration, work in self-regulated groups and complete and challenging tasks. The findings support the hypothesis that work-oriented design concepts are related to higher efficiency and better achievement of goals and pursued with the use of new technologies. In the third phase 12 companies have been selected for detailed case studies: The companies are comparable concerning product range and manufacturing conditions but different on the level of work-orientation.},
	keywords = {Computer-Integrated-Manufacturing CIM, Organizational design, Production design concepts, Socio-technical system approach, Work psychology, Work-orientation},
	author = {C. Kirsh and O. Strohm and E. Ulich}
}
