@article {Dogru2020,
	title = {AI in operations management: applications, challenges and opportunities},
	journal = {Journal of Data, Information and Management},
	volume = {2},
	number = {2},
	year = {2020},
	pages = {67{\textendash}74},
	publisher = {Journal of Data, Information and Management},
	abstract = {We have witnessed unparalleled progress in artificial intelligence (AI) and machine learning (ML) applications in the last two decades. The AI technologies have accelerated advancements in robotics and automation, which have significant implications on almost every aspect of businesses, and especially supply chain operations. Supply chains have widely adopted smart technologies that enable real-time automated data collection, analysis, and prediction. In this study, we review recent applications of AI in operations management (OM) and supply chain management (SCM). Specifically, we consider the innovations in healthcare, manufacturing, and retail operations, since collectively, these three areas represent a majority of the AI innovations in business as well as growing problem areas. We discuss primary challenges and opportunities for utilizing AI in those industries. We also discuss trending research topics with significant value potential in these areas.},
	keywords = {AI, artificial intelligence, Artificial Intelligence (AI), automation, machine learning, machine learning (ML), ml, om, operations management, Operations Management (OM), Robotics, scm, supply chain management, Supply Chain Management (SCM)},
	issn = {2524-6356},
	doi = {10.1007/s42488-020-00023-1},
	author = {Dogru, Ali K and Keskin, Burcu B}
}
@article {Roccetti2020,
	title = {A Cautionary Tale for Machine Learning Design: why we Still Need Human-Assisted Big Data Analysis},
	journal = {Mobile Networks and Applications},
	volume = {25},
	number = {3},
	year = {2020},
	pages = {1075{\textendash}1083},
	publisher = {Mobile Networks and Applications},
	abstract = {Supervised Machine Learning (ML) requires that smart algorithms scrutinize a very large number of labeled samples before they can make right predictions. And this is not always true either. In our experience, in fact, a neural network trained with a huge database comprised of over fifteen million water meter readings had essentially failed to predict when a meter would malfunction/need disassembly based on a history of water consumption measurements. With a second step, we developed a methodology, based on the enforcement of a specialized data semantics, that allowed us to extract only those samples for training that were not noised by data impurities. With this methodology, we re-trained the neural network up to a prediction accuracy of over 80\%. Yet, we simultaneously realized that the new training dataset was significantly different from the initial one in statistical terms, and much smaller, as well. We had reached a sort of paradox: We had alleviated the initial problem with a better interpretable model, but we had changed the replicated form of the initial data. To reconcile that paradox, we further enhanced our data semantics with the contribution of field experts. This has finally led to the extrapolation of a training dataset truly representative of regular/defective water meters and able to describe the underlying statistical phenomenon, while still providing an excellent prediction accuracy of the resulting classifier. At the end of this path, the lesson we have learnt is that a human-in-the-loop approach may significantly help to clean and re-organize noised datasets for an empowered ML design experience.},
	keywords = {Human-in-the-loop methods, Human-machine-Bigdata interaction loop, Machine learning design, Smart data, Water metering and consumption},
	issn = {15728153},
	doi = {10.1007/s11036-020-01530-6},
	author = {Roccetti, Marco and Delnevo, Giovanni and Casini, Luca and Salomoni, Paola}
}
@article {Acemoglu2020,
	title = {Competing with Robots: Firm-Level Evidence from France},
	journal = {AEA Papers and Proceedings},
	volume = {110},
	year = {2020},
	pages = {383{\textendash}388},
	abstract = {We study the firm-level implications of robot adoption in France. Of 55,390 firms in our sample, 598 adopted robots between 2010 and 2015, but these firms accounted for 20 percent of manufacturing employment. Adopters experienced significant declines in labor shares, the share of production workers in employment, and increases in value added and productivity. They expand their overall employment as well. However, this expansion comes at the expense of competitors, leading to an overall negative association between adoption and employment. Robot adoption has a large impact on the labor share because adopters are larger and grow faster than their competitors.},
	keywords = {automation, competition, j23, j24, jel codes, l11, labor share, manufacturing, productivity, reallocation, robots, tasks},
	issn = {2574-0768},
	doi = {10.1257/pandp.20201003},
	author = {Acemoglu, Daron and Lelarge, Claire and Restrepo, Pascual}
}
@article {Huang2020,
	title = {Engaged to a Robot? The Role of AI in Service},
	journal = {Journal of Service Research},
	year = {2020},
	abstract = {This article develops a strategic framework for using artificial intelligence (AI) to engage customers for different service benefits. This framework lays out guidelines of how to use different AIs to engage customers based on considerations of nature of service task, service offering, service strategy, and service process. AI develops from mechanical, to thinking, and to feeling. As AI advances to a higher intelligence level, more human service employees and human intelligence (HI) at the intelligence levels lower than that level should be used less. Thus, at the current level of AI development, mechanical service should be performed mostly by mechanical AI, thinking service by both thinking AI and HI, and feeling service mostly by HI. Mechanical AI should be used for standardization when service is routine and transactional, for cost leadership, and mostly at the service delivery stage. Thinking AI should be used for personalization when service is data-rich and utilitarian, for quality leadership, and mostly at the service creation stage. Feeling AI should be used for relationalization when service is relational and high touch, for relationship leadership, and mostly at the service interaction stage. We illustrate various AI applications for the three major AI benefits, providing managerial guidelines for service providers to leverage the advantages of AI as well as future research implications for service researchers to investigate AI in service from modeling, consumer, and policy perspectives.},
	keywords = {artificial intelligence, augmentation, automation, engagement, feeling AI, human intelligence, mechanical AI, personalization, relationalization, replacement, robots, service process, service strategy, standardization, thinking AI},
	issn = {15527379},
	doi = {10.1177/1094670520902266},
	author = {Huang, Ming Hui and Rust, Roland T}
}
@article {GomezdeAgreda2020,
	title = {Ethics of autonomous weapons systems and its applicability to any AI systems},
	journal = {Telecommunications Policy},
	volume = {5},
	number = {5},
	year = {2020},
	pages = {101953},
	publisher = {Elsevier Ltd},
	abstract = {Most artificial intelligence technologies are dual-use. They are incorporated into both peaceful civilian applications and military weapons systems. Most of the existing codes of conduct and ethical principles on artificial intelligence address the former while largely ignoring the latter. But when these technologies are used to power systems specifically designed to cause harm, the question must be asked as to whether the ethics applied to military autonomous systems should also be taken into account for all artificial intelligence technologies susceptible of being used for those purposes. However, while a freeze in investigations is neither possible nor desirable, neither is the maintenance of the current status quo. Comparison between general-purpose ethical codes and military ones concludes that most ethical principles apply to human use of artificial intelligence systems as long as two characteristics are met: that the way algorithms work is understood and that humans retain enough control. In this way, human agency is fully preserved and moral responsibility is retained independently of the potential dual-use of artificial intelligence technology.},
	keywords = {AI ethics, Autonomous weapons, CCW, Dual-use AI, Explainability, Meaningful human control},
	issn = {03085961},
	doi = {10.1016/j.telpol.2020.101953},
	url = {https://doi.org/10.1016/j.telpol.2020.101953},
	author = {G{\'o}mez de {\'A}greda, {\'A}ngel}
}
@article {Hacker2020,
	title = {Explainable AI under contract and tort law: legal incentives and technical challenges},
	journal = {Artificial Intelligence and Law},
	number = {0123456789},
	year = {2020},
	publisher = {Springer Netherlands},
	abstract = {This paper shows that the law, in subtle ways, may set hitherto unrecognized incentives for the adoption of explainable machine learning applications. In doing so, we make two novel contributions. First, on the legal side, we show that to avoid liability, professional actors, such as doctors and managers, may soon be legally compelled to use explainable ML models. We argue that the importance of explainability reaches far beyond data protection law, and crucially influences questions of contractual and tort liability for the use of ML models. To this effect, we conduct two legal case studies, in medical and corporate merger applications of ML. As a second contribution, we discuss the (legally required) trade-off between accuracy and explainability and demonstrate the effect in a technical case study in the context of spam classification.},
	keywords = {Contract law, Corporate takeovers, Explainability, Explainability-accuracy trade-off, Explainable AI, Interpretable machine learning, Medical malpractice, Tort law},
	isbn = {0123456789},
	issn = {15728382},
	doi = {10.1007/s10506-020-09260-6},
	url = {https://doi.org/10.1007/s10506-020-09260-6},
	author = {Hacker, Philipp and Krestel, Ralf and Grundmann, Stefan and Naumann, Felix}
}
@article {Reid-musson2020,
	title = {Feminist economic geography and the future of work},
	journal = {EPA: Economy and Space},
	year = {2020},
	pages = {1{\textendash}12},
	keywords = {corresponding author, department of geography, emily reid-musson, feminist economic geography, memorial university of newfoundland, newfoundland and labrador a1c, social reproduction, subjectivity, technology, work},
	doi = {10.1177/0308518X20947101},
	author = {Reid-musson, Emily and Cockayne, Daniel and Frederiksen, Lia and Worth, Nancy}
}
@proceedings {9999,
	title = {Impacts of the Use of Machine Learning on Work Design},
	year = {2020},
	month = {11/2020},
	publisher = {ACM},
	address = {Virtual Event, NSW, Australia},
	abstract = {<p>The increased pervasiveness of technological advancements in automation makes it urgent to address the question of how work is changing in response. Focusing on applications of machine learning (ML) to automate information tasks, we draw on a simple framework for identifying the impacts of an automated system on a task that suggests 3 patterns for the use of ML{\textemdash}decision support, blended decision making and complete automation. In this paper, we extend this framework by considering how automation of one task might have implications for interdependent tasks and how automation applies to coordination mechanisms.</p>},
	keywords = {artificial intelligence, automation, Coordination, machine learning, work design},
	isbn = {978-1-4503-8054-6/20/11},
	doi = {10.1145/3406499.3415070},
	attachments = {https://waim.network/sites/crowston.syr.edu/files/Impacts_of_ML_for_HAI_2020.pdf},
	author = {Kevin Crowston and Bolici, Francesco}
}
@article {Capatina2020,
	title = {Matching the future capabilities of an artificial intelligence-based software for social media marketing with potential users{\textquoteright} expectations},
	journal = {Technological Forecasting and Social Change},
	volume = {151},
	number = {June 2019},
	year = {2020},
	pages = {119794},
	publisher = {Elsevier},
	abstract = {The increasing use of Artificial Intelligence (AI) in Social Media Marketing (SMM) triggered the need for this research to identify and further analyze such expectations of potential users of an AI-based software for Social Media Marketing; a software that will be developed in the next two years, based on its future capabilities. In this research, we seek to discover how the potential users of this AI-based software (owners and employees from digital agencies based in France, Italy and Romania, as well as freelancers from these countries, with expertise in SMM) perceive the capabilities that we offer, as a way to differentiate our technological solution from other available in the market. We propose a causal model to find out which expected capabilities of the future AI-based software can explain potential users{\textquoteright} intention to test and use this innovative technological solution for SMM, based on integer valued regression models. With this purpose, R software is used to analyze the data provided by the respondents. We identify different causal configurations of upcoming capabilities of the AI-based software, classified in three categories (audience, image and sentiment analysis), and will trigger potential users{\textquoteright} intention to test and use the software, based on an fsQCA approach.},
	keywords = {artificial intelligence, Audience analysis, Image analysis, machine learning, Sentiment analysis, Social media marketing},
	issn = {00401625},
	doi = {10.1016/j.techfore.2019.119794},
	url = {https://doi.org/10.1016/j.techfore.2019.119794},
	author = {Capatina, Alexandru and Kachour, Maher and Lichy, Jessica and Micu, Adrian and Micu, Angela Eliza and Codignola, Federica}
}
@article {Wagner2020,
	title = {The nature of the Artificially Intelligent Firm - An economic investigation into changes that AI brings to the firm},
	journal = {Telecommunications Policy},
	volume = {44},
	number = {6},
	year = {2020},
	pages = {101954},
	publisher = {Elsevier Ltd},
	abstract = {With the arrival of Artificial Intelligence (AI), the nature of the firm is changing and economic theory can provide guidance to businesses as well as to politics when formulating adequate strategies for this unknown terrain. By interpreting AI as a new type of agent within the firm, the theory of the firm can serve as a lingua franca to connect computer sciences and social sciences when dealing with the interdisciplinary phenomenon of AI. To achieve this, this paper adopts the perspective of the economic theory of the firm to systematically explore the changes that AI brings to the institution of the firm. In total, five interrelated propositions are discussed that are rooted in the traditional theory but trace the nature of the Artificially Intelligent Firm: AI intensifies the effects of economic rationality on the firm (1). AI introduces a new type of information asymmetry (2). AI can perforate the boundaries of the firm (3). AI can create triangular agency relationships (4) and AI has the potential to remove traditional limits of integration (5).},
	keywords = {artificial intelligence, Asymmetric information, machine learning, Principal-agent problem, Theory of the firm},
	issn = {03085961},
	doi = {10.1016/j.telpol.2020.101954},
	url = {https://doi.org/10.1016/j.telpol.2020.101954},
	author = {Wagner, Dirk Nicolas}
}
@article {Theresa2020,
	title = {Think with me, or think for me? On the future role of artificial intelligence in marketing strategy formulation},
	journal = {The TQM Journal},
	volume = {ahead-of-p},
	number = {ahead-of-print},
	year = {2020},
	month = {jan},
	abstract = {Purpose This paper explores if and how Artificial Intelligence can contribute to marketing strategy formulation.Design/methodology/approach Qualitative research based on exploratory in-depth interviews with industry experts currently working with artificial intelligence tools.Findings Key themes include: (1) Importance of AI in strategic marketing decision management; (2) Presence of AI in strategic decision management; (3) Role of AI in strategic decision management; (4) Importance of business culture for the use of AI; (5) Impact of AI on the business{\textquoteright} organizational model. A key consideration is a {\textquotedblleft}creative-possibility perspective,{\textquotedblright} highlighting the future potential to use AI not only for rational but also for creative thinking purposes.Research limitations/implications This work is focused only on strategy creation as a deliberate process. For this, AI can be used as an effective response to the external contingencies of high volumes of data and uncertain environmental conditions, as well as being an effective response to the external contingencies of limited managerial cognition. A key future consideration is a {\textquotedblleft}creative-possibility perspective.{\textquotedblright}Practical implications A practical extension of the Gartner Analytics Ascendancy Model (Maoz, 2013).Originality/value This paper aims to contribute knowledge relating to the role of AI in marketing strategy formulation and explores the potential avenues for future use of AI in the strategic marketing process. This is explored through the lens of contingency theory, and additionally, findings are expressed using the Gartner analytics ascendancy model.},
	keywords = {AI, artificial intelligence, creativity, marketing strategy, marketing synergy, paper type research paper, rationality, tqm},
	isbn = {1754-2731},
	doi = {10.1108/TQM-12-2019-0303},
	url = {https://doi.org/10.1108/TQM-12-2019-0303},
	author = {Theresa, Eriksson and Alessandro, Bigi and Michelle, Bonera and Eriksson, Theresa and Bigi, Alessandro and Bonera, Michelle and Theresa, Eriksson and Alessandro, Bigi and Michelle, Bonera}
}
@proceedings {9999,
	title = {Impacts of machine learning on work},
	year = {2019},
	address = {Wailea, HI},
	abstract = {<p>The increased pervasiveness of technological advancements in automation makes it urgent to address the question of how work is changing in response. Focusing on applications of machine learning (ML) that automate information tasks, we present a simple framework for identifying the impacts of an automated system on a task. From an analysis of popular press articles about ML, we develop 3 patterns for the use of ML--decision support, blended decision making and complete automation--with implications for the kinds of tasks and systems. We further consider how automation of one task might have implications for other interdependent tasks. Our main conclusion is that designers have a range of options for systems and that automation of tasks is not the same as automation of work.</p>},
	keywords = {artificial intelligence, automation, machine learning, work design},
	doi = {10.24251/HICSS.2019.719},
	url = { http://hdl.handle.net/10125/60031},
	attachments = {https://waim.network/sites/crowston.syr.edu/files/Impacts_of_machine_learning_on_work__revision_.pdf},
	author = {Kevin Crowston and Bolici, Francesco}
}
@article {2018,
	title = {Artificial Intelligence in Medicine and Radiation Oncology},
	journal = {Cureus},
	year = {2018},
	month = {Jan-04-2019},
	abstract = {Artificial Intelligence (AI) was reviewed with a focus on its potential applicability to radiation oncology. The improvement of process efficiencies and the prevention of errors were found to be the most significant contributions of AI to radiation oncology. It was found that the prevention of errors is most effective when data transfer processes were automated and operational decisions were based on logical or learned evaluations by the system. It was concluded that AI could greatly improve the efficiency and accuracy of radiation oncology operations.},
	keywords = {artificial intelligence, big data, error analysis, error prevention, machine learning, process efficiency, process optimization, quality improvement, radiation oncology},
	doi = {10.7759/cureus.2475},
	url = {https://www.cureus.com/articles/11443-artificial-intelligence-in-medicine-and-radiation-oncology},
	author = {Weidlich, Vincent and Weidlich, Georg A.}
}
@article {2018,
	title = {Artificial intelligence, machine learning and the evolution of healthcare A bright future or cause for concern?},
	journal = {British Journal of Radiology},
	volume = {7},
	year = {2018},
	pages = {223-225},
	type = {Editorial},
	keywords = {artificial intelligence, deep learning, machine learning},
	author = {L.D. Jones and D. Golan and S. A. Hanna and M. Ramachandran}
}
@article {2018,
	title = {Automated Classification of chest X-ray images as normal or abnormal using Convolutional Neural Network},
	journal = {Asian Journal of Convergence in Technology},
	volume = {4},
	year = {2018},
	month = {04/2018},
	abstract = {Chest X-Rays are generally used for diagnosing abnormalities in the thoracic area. Radiologists need to spend significant amount of time for interpreting scans. Automatic classification of these images could greatly help radiology interpretation process by enhancing real world diagnosis of problems. Hence, radiologists can focus on detecting abnormalities from the abnormal images rather than checking for it in all the images. In this paper, we present a machine learning approach to solve this problem. Here, the algorithm uses COnvolutional Neural Networks (CNN) to learn and classify chest X-ray images as normal or abnormal based on image features.},
	keywords = {classification, machine learning, radiology},
	author = {Aayushi Gupta and Anupama C and P Indumathi and Anuj Kumar}
}
@article {2018,
	title = {The discourse approach to boundary identification and corpus construction for theory review articles},
	journal = {Journal of the Association for Information Systems},
	year = {2018},
	keywords = {article identification, boundary identification, citation search, keyword search, literature review, machine learning, research review, review article},
	url = {https://www.researchgate.net/publication/325215971_Understanding_the_Elephant_The_Discourse_Approach_to_Boundary_Identification_and_Corpus_Construction_for_Theory_Review_Articles},
	author = {Kai R. Larsen and Dirik S. Hovorka and Alan R. Dennis and Jevin D. West}
}
@article {2018,
	title = {How real is the impact of artificial intelligence? The business information survey 2018},
	journal = {Business Information Review},
	volume = {35},
	year = {2018},
	month = {12/2019},
	pages = {99 - 115},
	keywords = {Artificial Intelligence (AI), blockchain, chatbot, cybersecurity, data economy, data governance, data lakes, data literacy, data quality, data trusts, data value, ethics, information literacy, intelligent virtual agents, machine learning (ML), Robotics},
	issn = {0266-3821},
	doi = {10.1177/0266382118790150},
	url = {http://journals.sagepub.com/doi/10.1177/0266382118790150},
	author = {Carter, Denise}
}
@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}
}
@conference {2017,
	title = {Are We Safe Enough in the Future of Artificial Intelligence? A Discussion on Machine Ethics and Artificial Intelligence Safety},
	booktitle = {Scientific Methods in Academic Research and Teaching International Conference},
	year = {2017},
	pages = {184-197},
	abstract = {Nowadays, there is a serious anxiety on the existence of dangerous intelligent systems and it is not just a science-fiction idea of evil machines like the ones in well-known Terminator movie or any other movies including intelligent robots - machines threatening the existence of humankind. So, there is a great interest in some alternative research works under the topics of Machine Ethics, and Existential Risks. The objective of this study is to provide a general discussion about the expressed research topics and try to find some answers to the question of {\textquoteright}Are we safe enough in the future of Artificial Intelligence?{\textquoteright}. In detail, the discussion includes a comprehensive focus on {\textquoteright}dystopic{\textquoteright} scenarios, enables interested researchers to think about some {\textquoteright}moral dilemmas{\textquoteright} and family have some ethical outputs that are considerable for developing good intelligent systems. From a general perspective, the discussion taken here is a good opportunity to improve awareness on the mentioned, remarkable research topics associated with not only Artificial Intelligence but also many other natural and social sciences taking role in the humankind},
	keywords = {artificial intelligence, artificial intelligence safety, future of artificial intelligence, machine ethics, machine learning},
	author = {Utku  K{\"o}se}
}
@article {2017,
	title = {Will Life Be Worth Living in a World Without Work? Technological Unemployment and the Meaning of Life},
	journal = {Science and Engineering Ethics},
	volume = {23},
	year = {2017},
	publisher = {Springer},
	chapter = {41-64},
	abstract = {Suppose we are about to enter an era of increasing technological unemployment. What implications does this have for society? Two distinct ethical/social issues would seem to arise. The first is one of distributive justice: how will the (presumed) efficiency gains from automated labour be distributed through society? The second is one of personal fulfillment and meaning: if people no longer have to work, what will they do with their lives? In this article, I set aside the first issue and focus on the second. In doing so, I make three arguments. First I argue that there are good reasons to embrace non-work and that these reasons become more compelling in an era of technological unemployment. Second, I argue that the technological advances that make widespread technological unemployment possible could still threaten or undermine human flourishing and meaning, especially if (as is to be expected) they do not remain confined to the economic sphere. And third, I argue that this threat could be contained if we adopt an integrative approach to our relationship with technology. In advancing these arguments, I draw on three distinct literatures: (1) the literature on technological unemployment and workplace automation; (2) the antiwork critique - which I argue gives reasons to embrace technological unemployment; and (3) the philosophical debate about the conditions for meaning in life - which I argue gives reasons for concern.},
	keywords = {Antiwork, automation, Egalitarianism, Freedom, Meaning of life, Technological unemployment, Transhumanism},
	doi = {10.1007s/11948-016-9770-5},
	author = {John Danaher}
}
@article {2016,
	title = {Human Interaction With Robot Swarms: A Survey},
	journal = {Transaction on human-machine systems},
	volume = {46},
	year = {2016},
	month = {02/2016},
	pages = {9-26},
	publisher = {IEEE},
	abstract = {Recent advances in technology are delivering robots of reduced size and cost. A natural outgrowth of these advances are systems comprised of large numbers of robots that collaborate autonomously in diverse applications. Research on effective autonomous control of such systems, commonly called swarms, has increased dramatically in recent years and received attention from many domains, such as bioinspired robotics and control theory. These kinds of distributed systems present novel challenges for the effective integration of human supervisors, operators, and teammates that are only beginning to be addressed. This paper is the first survey of human-swarm interaction (HSI) and identifies the core concepts needed to design a human-swarm system. We first present the basics of swarm robotics. Then, we introduce HSI from the perspective of a human operator by discussing the cognitive complexity of solving tasks with swarm systems. Next, we introduce the interface between swarm and operator and identify challenges and solutions relating to human-swarm communication, state estimation and visualization, and human control of swarm. For the latter, we develop a taxonomy of control methods that enable operators to control swarms effectively. Finally, we synthesize the results to highlight remaining challenges, unanswered questions, and open problems for HSI, as well as how to address them in future works.},
	keywords = {Human-robot interaction (HRI), human-swarm interaction (HSI), multi-robot systems, swarm robotics}
}
